Can You Predict Stocks and Forex Market Price?
I can not predict the market price. Efficient market nobody can predict. But I or you – we can make an observation, opinion, strategy – assumption. But I believe that the market is inefficient in some moments, and then we traders can take the opportunity.
How to predict forex?
Forex price prediction represents the idea that history may repeat itself in predictable patterns. Forex price prediction is based on price levels analysis and building models (regression and classification models ) based on price history data, technical and fundamental indicators.
To predict the forex movement in foreign exchange rates using past market data, traders need to look for patterns and analyze important price levels.
When traders want to predict the forex or stocks market, they can only create some models (using statistics to predict outcomes); when you create a set of rules for forex trading, you create the rule-based trading model. Anyway, traders can try to make predictive modeling, test observations, validate opinions. Still, in the end, they always figure out that the market is a very complicated mathematical problem that is very hard to solve. This is the best characteristic of the market – strong liquidity, a lot of players, and the fact that all most impossible to predict the next outcome.
Why is that efficient market and “random walk” good?
The answer is that a truly efficient market eliminates the possibility of beating the market because any information available to any trader is already incorporated into the market price.
Have you noticed that you have been doing a lot of wondering about whether you can predict stocks and forex market? In terms of stock market prediction, this entails assaying to determine the value of a company’s stock within the context of a future timeframe or some other type of financial asset that is traded via the usage of an exchange. When a prediction turns out to be successful about a stock or forex currency pair’s future price, this could result in a trader’s high-profit level. It is noted that the efficient market hypothesis tends to suggest that prices are responsible for reflecting all information available at present in conjunction with all changes in pricing that are not linked to new data are therefore deemed as being not predictable. Others may hold a disagreeing point of view about this case. Those who favor the point of view about such a case are also realized as implementing a wide spectrum of approaches and technologies that supposedly enable them to access price information regarding the future to hope to achieve large sums of profits.
The Hypothesis of Efficient Markets and the Link to the Random Walk
It is noted that the hypothesis of efficient markets puts forth the assumption that prices are an element of information and expectations that are rational. It also considers information that is newly produced concerning a company’s prospects that usually reflects the price’s context at present. This implies that all information that is readily available to the public concerning a company, which is noted as taking into inclusion the history of the pricing, would truly be provided in the reflection of the stock’s pricing at present. Therefore, it is understood that the changes in the price do indeed contribute to making an impact on the provision of new data, general changes that happen in the market, and movements of randomness regarding the value that reflects the present data set. Burton Malkiel is reputed as having written a powerful piece called A Random Walk Down Wall Street, published in 1973. He declared that it was impossible to make accurate predictions about pricing by considering only the pricing history. This led to Malkiet presenting the claim that prices are better directed under a process that is statistical in essence. He labeled this as the random walk, which means that each day’s deviations away from the center position’s value are classified as being random. This leads to the rationalization that they cannot be predicted. Malkiel then went on to provide the conclusion that the net portfolio was more hindered than helped when someone engaged in granting payment to financial service providers for the sake of making predictions concerning the market. The application of several empirical tests tends to indicate that the theory has a wide application since most portfolios under the management of professional stock predictors tend not to outperform the average return of the market following the application of the manager’s fees.
Though the reality is that many financial academics tend to hold a favorable perspective toward the hypothesis of an efficient market, the critics of this hypothesis provide indications of scenarios where the reality of the experience of the market was not the same as what the hypothesis implies in terms of the prediction of unpredictability. It is noted that there is an enormous industry that has sprung up that proposes the idea that there are some analysts who are better in terms of making predictions in comparison to others. Paradoxically, this is considered an impossible feat when applying the hypothesis of efficient markets if the stock prediction industry did not engage in providing the essence of something that customers thought to be beneficial. It is further realized that a highly notorious investor who had achieved much success, noted as Warren Buffet, claimed that the evidence of the efficient market hypothesis is false when he presented a speech in the year 1984 at Columbia University.
Intrinsic Value
Intrinsic value means that something has real value. This is the value that a company is perceived to have. This value can be calculated. It considers the elements that are both tangible and intangible during the process of conducting the fundamental analysis. It is also a common practice to refer to the intrinsic value as the fundamental value. This value is applied to compare with the company’s market value and address if the company has undergone undervaluation in terms of its place on the stock market. When conducting the calculation of the intrinsic value, the investor will consider the business’s quantitative elements in conjunction with the business’s qualitative elements. Normally, the intrinsic value undergoes calculation via the provision of the sum of the discounted future income yielded via the assets to derive the value at the current time.
Methods of Prediction
Methodologies for engaging in predictions are regarded as being placed in three-wide classifications, noted as frequently overlapping. Thus, these methodologies are recognized as fundamental analysis, technical analysis, and technological methods.
Fundamental Analysis and market prediction
Fundamental analysts are interested in the company that engages in underlying the stock of its own merit. They evaluate the company’s past performance and the credibility of the accounts that the company possesses. There is the creation of several performance ratings to help the fundamental analyst assess its validity, such as using the P/E ratio. Warren Buffet is regarded as being a truly famous fundamental analysis. He applies the comprehensive ratio of market capitalization to GDP to indicate the general relative value regarding the stock market. As a result, this ratio is also referred to as the Buffet ratio.
Technical Analysis and market prediction
Technical analysts are not interested in the fundamental elements of a company. They engage in determining the future pricing about a stock founded primarily on the pricing history trends, which is regarded as a form of time-series analytics. A wide spectrum of patterns is applied, such as the head and shoulders. Also, it is common to use the cup and saucer. In conjunction with the usage of patterns, there is also the usage of techniques. For example, one such technique is regarded as being the exponential moving average. Also, oscillators use oscillators, levels of support, resistance, and indicators of momentum and volume. At this present time, it is a popular practice to use candlestick patterns, which were likely created by Japanese merchants. Technical analysis tends to be applied in the case of short term strategies rather than long term strategies. With this being the case, this type of analysis is more prevalently used in the areas of commodities and forex markets when traders concentrate on the movements of prices in the short term. This analysis often applies the usage of some key assumptions. The first assumption is that all noteworthy components concerning a company are reflected in the stock price. The other assumption is that the price moves according to the trends. Finally, another assumption indicates that the history of pricing usually experiences repetitions, which results from the market’s psychology.
Machine learning as a market prediction tool
As a result of the inception of computers, the prediction of the stock market and forex has shifted into the realm of machine learning.
In machine learning, we have features (economic parameters, technical indicators, price values, etc.) and target variables (close price, profit or loss, etc.).Using features, we try to create a model to predict the target variable.
f(Industrial production index, RSI, Volume, B. Bands, Price1, Price2, Price3, etc.) = Close Price
The most prevalent technique today applies the usage of artificial neural networks in association with genetic algorithms. Artificial neural networks can be considered as a type of mathematical element of approximators. The most prominent form of this, which is applied in predicting the stock market, is noted as the feedforward network that engages in the usage of the algorithm of backward propagation in terms of errors to update the network. Such networks are often addressed as backward propagation networks. Another type of artificial neural network that is considered to be better suited concerning the prediction of stocks is the recurrent neural network based on time. Also, there is the usage of time-delay neural networks to derive the best possible predictions with the hope of earning large profits.
Simple regression models are very often better than deep learning models in my experience. Simple trading models are usually as good as complicated machine learning models in the trading industry.
Anyway, in the future, we will have more articles on this subject.
Our goal is to find an interesting pattern, market inefficiency pattern, and to trade at that moment. It is hard, but possible.
He is an expert in financial niche, long-term trading, and weekly technical levels.
The primary field of Igor's research is the application of machine learning in algorithmic trading.
Education: Computer Engineering and Ph.D. in machine learning.
Igor regularly publishes trading-related videos on the Fxigor Youtube channel.
To contact Igor write on:
igor@forex.in.rs
