Emerging Theories on Binary Options Trading and Market Bubble Risk

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Why trading binary options and gambling are quite different things

Binary options attract more and more attention, because it is a great way of earning considering the simplicity of their usage in the buying process. Only imagine next situation: deal with expire time from 30 seconds and up to several months, approximately may make a profit of about 80% of the total cost of the option. This was the real reason for the rapid growth of interest in this type of trading in the financial market.

Is trading binary options gambling or it is completely different things? In order to really answer this question intelligently, at first, remind yourself what is gambling.

The goal of each and every gambling is targeted at inciting excitement in player. For example, the well-known card game “Poker”, which is all considered gambling. But does it really evoke strong emotions in people? In fact, an ardent gambler, she’s probably really excited, but, for example, for the dealer is nothing more than a job. Hardly professional croupiers will argue that he is actually a gambler. In any case it is a link that is definitely allotted role in this game and not much more. In other words, the perception of the game. It’s like in a hockey game that for fans this is a real game, and for players work.

Therefore, given the above induced opinion and taking into account he binary options market, where people perceive the Forex trading, as a game, therefore, likely to experience the passion. Therefore, it is logical to assume that his position will be similar to any gambling. At the same time, the person who objectively looks at things, has a real understanding of what is happening and to adequately assess their capabilities, never will be related to binary options trading as gambling. The essence of the perception, not in the lesson, and what people will do in a given situation.

Brewing logical question: why the gambler is not a bit of work on binary options? And it’s all quite simple:

• the player originally will be allocated to the trading process as a game — hence the action often will be ill-considered and frivolous;

• more than likely, these people will not learn anything, relying on fart and perceiving the learning process is a waste of time;

• high embernet the occurrence of excessive expectations, often will be associated with a profit in the first seconds.

BINARY OPTIONS AND GAMBLING

Very often you can hear that the options trading looks like the well known game “Roulette”, wherefore winning is impossible. First and foremost, to win here, obviously, the truth will fail, however, to start earning in a very real way. additives important here is the approach to this activity and its human perception. These two things resemble one another due to the fact that roulette as well as in options trading, you can make bets at random, or to calculate all possible combinations to remember past results and calculate the probability of an event.

A similar result in trading binary options — someone will come for the freebies, the result is playing will deposit lose, and someone will start to earn real money by earnestly studying, examining the experience of other successful traders, analyzing the trends and the situation of market, knowingly invented and use technical and fundamental analysis. Trader also needs to be armed for victory.

Anyone who seriously trades binary options never call it a game, because he would not a priori to experience the excitement. In fact, top traders, to achieve real success in this field, will always look at binary options trading soberly. After all, they really understand that the thrill of destruction. Hence, the person who wants does not want to play, but seriously to make money trading in the financial market, he must learn to take seriously, not to be confused with the conventional gambling. After all, the one who wants to, he will always find a 100% similarity in all this, mixing the result of trade and game, when in fact it is quite the different classes.

Little advice for everyone: if you have a feeling of unhealthy excitement during the trading process (similar to gamblers), then right immediately quit with it, because it is well known that emotions still haven’t helped to reach the desired results nobody.

Sadly, but truly – it is not suitable for everyone to trade on the financial market. But anyone with a desire can test himself and check luck, while not risking his own money.

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“General Risk Warning: Binary options trading carry a high level of risk and can result in the loss of all your funds.”

The Hidden Risks in Emerging Markets

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April 2020 Issue

The Idea in Brief

Governments in developing countries no longer seize foreign investments. Instead they find ways to divert the profits through regulation or selective lack of regulation. The costs are estimated to be equivalent to at least a 33% increase in tax.

Most of the traditional ways to limit risk to your company’s foreign investments are not very effective anymore. You now have to learn how to play politics directly.

As companies such as the Italian oil giant Eni have demonstrated, managing political risk involves balancing operational efficiency with political capital, mastering the art of political spin, and hitting the pressure points of local decision makers. That means mastering new analytic skills and tools.

When a firm with a value-generating technological or managerial capability invests abroad, its shareholders and the host country’s citizens both stand to benefit. But no matter how good the apparent fit between what foreign companies offer and what host countries need, success is far from assured. Elections and other political events, economic crises, and changing societal attitudes can disrupt the best-laid plans in both emerging and advanced economies. The interplay of these forces—and the implications for the political choices that multinational firms make—will become especially prominent as national governments chart an uncertain course toward stabilization following the global financial meltdown.

Issues such as taxation of executive compensation, the proper scope of financial regulation, and international M&A have come to the foreground in the wake of the crisis, and stark international differences in opinions and policies on these matters are already evident. The differences will only become more pronounced as discussions about the appropriate near-term policy response to the crisis give way to debates about who should pay and how much. Politicians will struggle to balance popular demands to punish those perceived as responsible against fears of stymied innovation and the flight of human and financial capital. Broader domestic economic concerns—for example, protectionist sentiment in response to the realignment of economic power in favor of emerging nations such as China and India—will inevitably affect the debate as well. The multinational firms best able to anticipate and manage the related risks and opportunities will have the strongest competitive edge.

Historically, the biggest risks faced by foreign investors were in developing countries with immature or volatile political systems. The chief concern was “expropriation risk,” the possibility that host governments would seize foreign-owned assets. Today, this risk has largely disappeared. Stronger international law and the symbiotic nature of growth in emerging and developed economies reduced asset seizures to nearly zero during the 1980s. However, as interest in emerging markets has soared, host countries have learned, according to George Chifor at the University of Windsor in Canada, “that more value can be extracted from foreign enterprises through the more subtle instrument of regulatory control rather than outright seizures.” The risk that a government will discriminatorily change the laws, regulations, or contracts governing an investment—or will fail to enforce them—in a way that reduces an investor’s financial returns is what we call “policy risk.”

Although the data on policy risk are less clear-cut than the hard numbers on direct seizures, press mentions of policy risk (using terms such as “political risk,” “political uncertainty,” “policy risk,” “policy uncertainty,” “regulatory risk,” and “regulatory uncertainty”) indicate that it has risen dramatically as seizure risk has fallen. (See the exhibit “The Changing Face of Risk in Emerging Markets.”) Press mentions of actual seizures have also increased somewhat since 2001, but that does not reflect a broad-based resurgence in seizures.

The Changing Face of Risk in Emerging Markets

Overt seizures of foreign assets by host countries in emerging markets essentially evaporated by 1980. However, other political risks to those assets (for example, from potential regulatory action) have risen dramatically since then.

Source: Seizure data (left side) from M.S. Minor, “The Demise of Expropriation as an Instrument of LDC Policy, 1980–1992,” Journal of International Business Studies 25 (1994): 177–88.

Other recent data are consistent with the finding that policy risk has increased greatly. A 2001 PriceWaterhouseCoopers study concluded that an opaque policy-making environment is equivalent to at least a 33% increase in taxation. A World Bank study in 2004 revealed that 15% to 30% of the contracts covering $371 billion of private infrastructure investment in the 1990s were subject to government-initiated renegotiations or disputes. And a 2009 survey by the Multilateral Investment Guarantee Agency and the Economist Intelligence Unit found that multinational enterprises considered breach of contract, restrictions on the transfer and convertibility of profits, civil disturbance, government failure to honor guarantees, and regulatory restrictions all to be more significant risks than the potential seizure of assets.

Unfortunately, the traditional financial and contractual mechanisms that firms use to assess and mitigate business risks have limited value. Therefore, investors must develop proactive political-management strategies that lessen government officials’ incentives to divert investors’ returns. In this article, we explore the experiences of multinational investors as they confront these issues in a variety of industries and countries, and we offer best-practice guidelines for assessing the political landscape and for modeling political decision making. As with the management of any risk or uncertainty, political mastery can become a source of competitive advantage in addition to a means of avoiding losses.

Political mastery can become a source of competitive advantage and a means of avoiding losses.

It’s Hard to Hedge Policy Risk

Firms engaged in international business often use some combination of legal contracts, insurance, and trade in financial instruments to protect the income streams from their investments against currency or price swings. These approaches, however, offer little protection against policy risk.

For starters, legal contracts are useful only if they are enforced, and shifting laws and regulations can render them void. In the 1990s many Southeast Asian governments wooing private power investors offered contracts that insulated the investors from risks related to lower-than-expected demand, fuel supplies, exchange rates, currency conversions, regulations, and political force majeure. The Asian financial crisis in 1997 brought those investors’ favorable treatment into sharp relief as currency values, share prices, and electricity demand all plummeted. Political officials had to choose between honoring the contracts, at the risk of compromising their own popular support, and renegotiating them in order to maintain that support. In the end, many career-minded public officials in Southeast Asia chose to renegotiate or cancel scores of contracts.

Even when contracts can be legally enforced, experience shows that inventive politicians can circumvent them, through a wide variety of means other than changing laws. For example, in 1998, when U.S.-based AES Corporation—then the world’s largest independent power company—acquired the Georgian electricity distribution company Telasi, high-priced lawyers constructed an ironclad set of guarantees that allowed AES-Telasi to pass the costs of policy and other risks on to Georgian consumers. One analyst remarked to us, “If you believed the contract, AES was guaranteed a 20% return on its investment.” The Georgian government actually never interfered formally with AES-Telasi’s ability to pass costs on to consumers. However, the venture was doomed by public officials’ inaction—for instance, their failure to terminate supply to nonpaying industrial consumers, to supply fuel to AES-Telasi, and to keep the government’s own account current—and by the government’s demand for tax payments on electricity for which the company had never been paid. The result was that AES’s “guaranteed” 20% return became a shareholder loss of $300 million.

Insurance offers limited protection against policy risk because a firm’s exposure is largely determined by its own ability to manage the policy-making process. In the words of one insurer: “I prefer to focus on what my assured [customer] can bring to a risk. My reasoning is that if you back the right assured, you can usually keep problems from occurring in the first place—and if they do happen, you have an excellent chance of mitigating your loss.” Yet it is very difficult for insurers to know who the “right assured” is, and the firms with the greatest risk exposure are often those most likely to seek insurance in the first place. As a result, underwriters price their products extremely high, offer very short-term coverage, or don’t offer any coverage at all.

Financial hedges have limited value for similar reasons. Instruments for hedging against risks in specific emerging markets—such as exchange-rate, market, and credit risks—are ubiquitous because multiple parties are willing to participate. The project- and firm-specific nature of policy risk, however, renders conventional hedging strategies infeasible.

Some of the more-inventive instruments are based on the average risk premium associated with existing companies in a given country—but they give false comfort. Because the baseline risk premiums are those of firms that are actively participating in a given market (and that often have their risk-mitigation strategies in place), new entrants are likely to face far greater exposure. In fact, foreign investors who focus on constructing financial hedges at the expense of developing their own risk-mitigation strategies may increase their exposure. It is therefore not surprising that, despite the ability to calculate residual risk premiums, no financial institutions have used such premiums to price an instrument that pays out money when a policy risk is realized.

The New Risk-Management Playbook

Given the difficulty of constructing hedges against policy risk through contracts, insurance, or financial risk-management tools, foreign investors must accept the responsibility for directly managing the risk themselves. For many companies, that means rewriting the playbook. Instead of looking for immediate ways to improve operations, managers have to move beyond the quick cost-benefit analyses that they usually undertake and think more about how they can frame and shape public debate. And they must learn how to apply political pressure, either individually or as part of a coalition.

Investing in goodwill.

In the developed world, managers spend a great deal of time and energy on improving efficiency. When companies move into less developed markets, they often expect huge, instant efficiency gains from exploiting the technologies, business models, and practices that they have managed to hone in their home markets. Unfortunately, the political costs of such practices may outweigh those gains.

Consider the 1997 Christmas blackout in large parts of Brazil, including Rio de Janeiro. The then recently privatized electric utility Light (in which AES held a 13.75% stake) faced record-high outdoor temperatures that week, and it was already struggling with poorly maintained equipment that had deteriorated before privatization. However, the press and the public focused on the 40% reduction in personnel, combined with the utility company’s record profits, to paint a picture of an exploitative foreign investor. The negative sentiment toward foreign firms in general and AES in particular contributed to the awarding of a 900-megawatt energy-supply contract the following spring to a joint venture led by Brazilian firms (Votorantim Group, Bradesco Group, and Camargo Corrêa) rather than consortia led by AES and British Gas.

A smarter approach was used by Italian state-owned oil company Eni. After the 1998 devaluation of the real, when many companies put their investment plans on hold or even exited Brazil, Eni’s then-CEO, Franco Bernabe, visited Rio de Janeiro to announce a $500 million investment. He proclaimed: “Now is the time to show that Petrobras [the state-owned oil company] has long-term friends.” Eni and Petrobras have collaborated closely ever since.

Framing the debate.

When companies enter new countries, they often engage in extensive PR campaigns that amount to little more than advertisements for the brand and specific commercial ventures. Instead, firms need to master the art of political spin. Presenting a venture as “fair,” “equitable,” or “growth enhancing” is often a simpler and more powerful means of securing political support than providing a cost-benefit analysis. The precise meaning attributed to such labels varies depending on a firm’s market position. New entrants garner support for policies that favor them over incumbents by citing the abuse of monopoly power. Conversely, dominant firms appeal to “fairness” by arguing that smaller entrants cannot survive without the government’s helping hand.

Instead of engaging in PR campaigns that amount to little more than advertisements for the brand, companies need to master the art of political spin.

This type of debate played out in the South Korean wireless market. LG Telecom—the third entrant, behind the much larger SK Telecom and Korea Telecom—made repeated calls for “asymmetric” government regulation of the market leaders in order to “level the playing field.” As the Korea Times reported, “The defining question is whether the government will back new entrants in the name of encouraging fair competition, or limit the pool to experienced players.” LG ultimately prevailed: In May 2001 the South Korean government announced that it would “guarantee a market share of at least 20% for a third major telecom operator through asymmetric regulation on Korea Telecom and SK Telecom.”

Finding political pressure points.

The network of relationships in a society greatly influences policy outcomes, especially in countries with weak legal systems. To turn these networks to their advantage, international investors must identify and engage local politicians’ power bases. Once again, Eni has shown the way, this time in Kazakhstan. Through its subsidiary Agip KCO, Eni has adopted a business model that responds to the former Soviet republic’s economic and social needs. The company favors Kazakh over non-Kazakh suppliers, and it conducts knowledge-transfer, training, and development seminars for them. At least 60% of local employees are Kazakh citizens. The company also funds the construction of various public works, including the national library, the prime minister’s residence, schools, computer labs, and multifamily housing units for the poor. As a result, many Kazakh officials now have a stake in Eni’s success.

For the vast majority of organizations—which do not possess enough leverage to influence the full range of relevant actors on their own—a crucial component of an effective strategy is to assemble a coalition of interests. In the South Korean wireless battle, LG Telecom benefited from the influence of upstream suppliers. The major Korean carriers wanted to shift to the globally favored WCDMA standard for the newest generation of cellular service, but domestic champion Samsung had developed a global leadership position in the competing CDMA2000 technology. Under pressure from Samsung, the government insisted that one of the new 3G licenses be awarded to LG Telecom in return for its promise to adopt CDMA2000.

An international investor’s home government can also be a powerful channel of influence. Observers in central Europe have noted the lobbying success of the German and French governments on behalf of national champions in countries seeking EU membership. However, the use of “foreign influence” may create a perception of meddling, can stoke nationalism, and is generally less likely to have a lasting impact. There’s also the risk that your home government will sacrifice your needs in order to gain traction on another issue.

Taking these pages out of the political playbook requires building the sorts of capabilities in intelligence gathering and analysis that are familiar to politicians, spies, and journalists. Managers must begin by understanding the attitudes, opinions, and positions of relevant actors toward their firm, the industry in which the firm operates, and any specific actions that the firm might take to influence outcomes on the playing field.

Tapping the Right Flow of Data

Traditionally, managers who have undertaken political analyses in a host country have directly consulted employees, local business partners, and supply chain partners. The information-gathering process varies in intensity and structure, ranging from surveying radio and newspaper stories to conversing with locals to using computerized contact-management systems. Some firms rely almost exclusively on informal chats, whereas others favor more-formal Delphi (iterative expert survey) methods. (Also see the sidebar “Why Country Risk Ratings Don’t Work.”)

Why Country Risk Ratings Don’t Work

When it comes to assessing levels of policy risk, managers are far too quick to rely on the subjective ratings of country “experts.” One popular index focuses on asset-seizure and contract-repudiation risks. Ratings are incorporated, in the form of country risk premiums, into the discount rates used to evaluate investment opportunities. This approach appears to have the formal rigor of financial risk management, but it is actually inadequate.

To begin with, such ratings usually fail to account for the fact that the levels of policy risk vary among different investors in a country, some of whom may adapt their business practices to local norms and lobby key policy makers better than others do. Also, policy-risk exposure is to some extent contingent on the relative importance of the proposed investment to the two parties (how easy is it for the firm to walk away, and how badly does the local government want the deal?). Finally, country risk ratings are usually retrospective, reflecting past policy outcomes. To assess the correlation with current policy risk, an analyst needs to determine how similar the past and present policy-shaping factors actually are.

Even as purely country-level measures, most political risk scorecards are woefully short on analysis, as an example from Chile and Indonesia clearly shows. In 1997, one risk index ascribed an identical score to those two countries. The measure took no account of the significant institutional differences between them. Faced with violent citizen demands to redistribute investor returns in the wake of the 1997 Asian financial crisis, Indonesia’s longtime military dictator, General Suharto, renegotiated contracts with foreign investors that were unaffiliated with his family or close friends. After he was ousted in a coup, the previously favored companies experienced a backlash as the successor government renegotiated their contracts.

Chile, in contrast, had a democratic multiparty system and possessed a well-respected independent judiciary—a further check against arbitrary policy change. Pressures in Chile to enhance equity and social cohesion culminated in the 2000 election of socialist Ricardo Lagos as president. He shifted some discretionary spending toward social programs but also respected the rule of law and existing commercial contracts. Underlying risks in Chile and Indonesia were therefore very different, but the country-level ratings didn’t reflect those distinct realities.

Although these sources provide valuable conventional input, they can require more time and money than such small, subjective, potentially biased snapshots might merit. Moreover, given the availability of multiple real-time indicators and metrics in functional areas such as finance, marketing, and human resources, CEOs and boards of directors increasingly demand similar real-time data on the preferences of key players. This human intelligence can be effectively and continuously incorporated into enterprise risk-management models and frameworks.

To broaden their perspectives, more and more companies are reaching out to nonbusiness organizations that can help them anticipate and preempt consumer concerns about environmental, health, and safety issues. For example, after a bruising experience over the disposal of its Brent Spar oil-drilling platform in 1995, Royal Dutch Shell now routinely includes Greenpeace in substantive environmental discussions. Some companies also consult professional experts, ranging from well-positioned ex-government officials operating on retainer; to the stringers who write for the Economist Intelligence Unit, Stratfor, and Oxford Analytica; to global political consultancies, such as Political Risk Services or Eurasia Group. Although employees, suppliers, and activists may have access to better information, they lack the specialized training that these advisers bring to the table.

Of increasing importance is the vast amount of information emanating from third-party sources—primarily the mainstream news media, but also bloggers and other observers—that routinely monitor the policy-making process in various countries. The large volume and relatively unfocused nature of the material make it hard to synthesize, digest, and act upon effectively, even if a company has substantial resources for this activity. However, with information-extraction software, it’s now possible to identify the relevant political and social actors on a given issue and their intensity of interest in it.

One approach, known as data mining, relies on the coincident location of words to derive information about key players’ preferences. For example, the occurrence of “Russia,” “AES-Telasi,” and “protest” in the same sentence implies a negative sentiment in the relationship between Russia and the electricity investor AES-Telasi. Another tool, called natural language parsing (NLP) software, facilitates more-refined sentence-level inferences by syntactically distinguishing among subjects, verbs, and objects, thereby identifying the orientation of actions or preferences. Consider this possible sentence: “The Union of Consumers of Georgia is outraged by the AES-Telasi American company proposal to increase the tariff on electric energy.” NLP software would recognize the precise grammatical relationship among “Union of Consumers of Georgia,” “is outraged by,” and “AES-Telasi…proposal”—pointing to a strong negative sentiment toward the U.S. company. NLP software can also gauge the intensity of sentiment. If the verb phrase in the sentence had been “objects to” instead of “is outraged by,” the software would have recognized that the sentiment of the Union of Consumers of Georgia toward AES was negative, but less so.

In 2003 Greenpeace activists in Hungary protested against the use of cyanide technology at the Rosia Montana gold mine in Romania.

Similarly, information-extraction tools can readily and objectively highlight shifts in an actor’s preferences over time. For example, a coalition of local and international activists sharply contested the plan by Canadian mining company Gabriel Resources to develop the Rosia Montana gold mine in Romania. The exhibit “Are the Locals Hostile to You?” plots the frequency in the worldwide media of sentences mentioning statements or actions against the mine by nongovernmental organizations through 2007, relative to the total number of sentences in articles about the mine during the same period. The data show that NGOs were relatively indifferent to the issue until mid-2002, when negative reports increased sharply.

Are the Locals Hostile to You?

Information-extraction software can capture changes in attitudes toward a business venture by syntactically analyzing the content of media reports about it. This example compares the total number of sentences in articles about Gabriel Resources’ plan to develop the Rosia Montana gold mine in Romania with the percentage of sentences indicating NGO opposition to the plan.

The “Tummy Test” and Other Models

With data about political actors and their level of interest in hand, managers must then synthesize that information into a model of the policy-making process. At the informal end of the synthesis spectrum is the “tummy test,” in which a decision maker who has spoken with or been briefed about relevant sources draws upon his or her own knowledge of similar cases to make an educated guess about the likely policy outcome. The accuracy of this technique clearly varies enormously according to the skill set of the decision maker and the relevance of his or her past experience to the current situation. To improve the accuracy of such judgments, managers can also involve specialized consultancies that draw upon a more diverse set of experiences from multiple firms and industries in the target country or a comparable one.

A sophisticated extension of the tummy test is the “war room,” in which managers come together for a one-off meeting or a series of brainstorming sessions. Sessions may be scheduled regularly or triggered by a shock or event that requires a strategic response. “Influence maps” are used to depict each politically relevant actor as a bubble arrayed in space according to the player’s position on a given issue, with the size of the bubble proportional to the player’s power. Linkages across actors or clusters of actors can be indicated by either location or connecting lines. Although no formal analytic tools are used, the maps can help guide discussion of action scenarios: What happens if we target actor X? What if we break the link between X and Y? What if we try to reduce Z’s power? The insights produced by this approach are, of course, only as good as the information brought into the room and the quality of the team assembled.

The most formal tool for modeling the policymaking process is the dynamic expected utility model, which is based on game theory. It assumes that, in each of several time periods, every actor (an individual or an organization) with a vested interest in an issue has a choice of three possible alternatives: proposing a policy, opposing a proposed policy, or doing nothing. Each actor chooses the alternative that maximizes his, her, or its expected utility in each period. The selection depends on the direction and intensity of the actor’s preferences, the salience of the issue, the cost of proposing or opposing a policy, and similar information about other actors. The combined actions of all the actors result in a likely policy outcome. The sensitivity of the outcome to various assumptions and parameters can then be calculated, helping to identify which actors are so pivotal that a change in their preferences, power, or salience would have a large impact on policy.

Models like this are widely used by the intelligence community and by specialist consulting groups such as Mesquita & Roundell, Sentia Group, the Probity Group, and Commetrix. A growing number of multinational corporations are also adopting these tools. A large British company, for example, used such a model to decide how to influence the climate change debate in the European Union. Analysts first identified which actors were most commonly cited in the press and whom these actors referenced in their speeches and writings. The analysts then constructed a network of key “influencers” and modeled various points of entry into this system to identify the target areas and the messages that would maximize their effect on the climate change debate.

Although the integration of automated data collection, dynamic expected utility modeling, and influence-map visualizations remains in its infancy, the potential applications are broader than the management of policy risk alone. Marketing research, financial analysis, operations, and human resources all could benefit from a richer analysis of the best ways to affect stakeholders’ opinions.

Of course, the risks of investment may simply be too great to justify entry into certain political zones. But in many cases investors who explicitly recognize the dynamism of the environment and implement appropriate strategies to address it will find the risks quite manageable. By combining data-mining and modeling technologies with traditional approaches, as we’ve described, they can start the journey forward, moving from “tummy tests” toward an analytically oriented, defensible system for managing policy risk that will greatly expand their investment options. At its heart, this system will always retain elements of tacit knowledge and experience, and not all managers and firms will be able to master its intricacies. But those that do will find it a powerful source of competitive advantage.

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Levels of support and resistance way to make money on binary options

Binary options and the levels is a separate set of strategies, the use of which in combination with the simplest technical indicators gives almost 90% of the performance. The only difficulty is determining the exact levels of support and resistance on different time frames. Error can lead to incorrect prediction so the trading strategy are complemented by indicators. Based on the boundary lines separate tools are composed — the “iron” levels, Fibonacci levels and other individual programmable indicator strategy. About the strategies based on them read more.

Rules for constructing levels in binary options

The principle of trading by binary options levels is that the trader analyzes the market on various time frames — large (full-temporal) and small (5-15 minutes). The period in the daily chart — year to minute — at least a month. On both charts the lines of support and resistance are drawn. Tactics are built on the following principles:

  • with high probability the price will stay within a narrow corridor bounded by borders. Possible small sample and the correction, because the system is based on placing a long timeframe. If the price runs from one border to the other in 15 minutes, the optimal expiration time — 8-10 minutes;
  • sooner or later there is a break. The task is to feel it and capitalize on the strong emerging trend.

To determine the breakdown of the iron levels the Line tool and confirming oscillators are used.

Strategies based on levels of support and resistance

Fibonacci Levels

Building a trading system based on the patterns. And if may seem that there are no regularities in the construction of lines of support and resistance, the mathematicians believe otherwise. Technical analysis is all built on mathematical models and formulas. And even if they do not always work, they are definitely more effective than probability theory.

It has long been observed that many natural phenomena evolve with a certain dependence. The proportions of the parts of the human body or plants formed in accordance with the ratio of 0.618. The numbers are not quite round, but rounding them is not recommended. The study of such ratios and proportions allowed mathematicians to create a sequence of numbers that in part reflect this dependence.

The Fibonacci sequence is a sequence of numbers, which reflects the price levels. Transcript of the boundaries is the following:

  • 23.6% — the first price value for which price may slow down or show local correction. Most often this value is not an obstacle for the trend, but may signal a potential change in direction. It is better not to open the trade, since the direction is difficult to predict;
  • 38.2% — area, in which you can open trades. Is a strong value in determining resistance and support. Once price direction will be clear, open a binary option with an expiration period of 1-3 hours;
  • 50% resistance line. Here is likely to be a rollback. Pending orders are the major players, the closure of which will turn quotes in the opposite direction. Here you can take the risk to open a non-standard option for touch (bounce);
  • 61.8% — the reduced target point. As practice shows, this way reach only the strongest players. The growth rate decays, and a breakdown means the beginning of a new strong trend. The use of this information allows you to adjust the trading system towards the direction of the price. If the rollback does not happen, we can safely open a binary option for purchase;
  • 76.4% of these put the safety stop-loss. If this price limit is breached, the binary option for purchase should be put with an expiration period to a value of 100%;
  • 100% — round target point, which will likely be a complete rollback of the trend. You can trade for a reversal.

Using these values, you can construct separate indicators for binary options working no worse than moving averages and other technical analysis tools. Fibonacci retracement levels show you possible turning points and potential magnitude of correction. The larger the timeframe, the greater the resistance these levels have. And now about how to use these levels.

Principles of trading on Fibonacci levels:

  • waiting for the appearance of a new trend after the correction and wait when price increase strikes a value of 50%. Mentally we put a bound on the value of 61.8% (iron level);
  • border of 61.8% means more probability of a reversal. If it happened, you can open a binary option on the slide and use the power of the movement;
  • a perfect level – line, which was tested a few times, but not broken.

Basic errors when trading on Fibonacci levels:

  • binary turbo options trading. No tactics, built on the patterns will be working over short time intervals. Expiration — at least 3-4 bars of working timeframe. The less time analyzed chart, the less accurate signals to win back the price momentum instead of rolling back;
  • on short trading intervals brokers set low profit margins (70%), if the strikes are close to Fibonacci levels. Since the probability of error is also present, optimal from the point of view of risk, yield — 80% and above;
  • do not open trades on the reversal from the 23.6% level. This is likely a correction, in this sense, the market is in uncertainty and movement in the direction of 50% will mean the stabilization of the market. The only exception is a long test line, but appears very rare.

To use the Fibonacci tools can anyone who is willing all year round to spend at the computer, tracking the correction of the trend. This is probably the only drawback of the strategy. To build the levels is possible on a demo account to determine a convenient time interval and to select a currency pair that best fulfills the mathematical pattern of trade.

Summary. Levels of support and resistance is a tool which can be used as the main tactic with confirming oscillators. Trade through the levels allows you to build a strategy to determine a strong trend and rebound in the potential reversal points. Once the reversal happens, from tested level, you can confidently open binary options in the direction of the turn. Strategies work well in volatile pairs, and also at the time of the news release, in a flat they can give the wrong signals. Flat recommend you to build levels using the Line tool and open transaction on the principle of channel tactics. If someone has comments and opinions, I invite to discussion after the article!

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