One of the most important analysis methods for predicting future price movements in financial markets is statistical analysis, which combines historical data such as volume and price to predict the price movement in the next step. Through this detailed guide we have prepared for you, you will be able to easily access all kinds of information about the definition of statistical analysis, how it works, its advantages and disadvantages, and its importance and types.
What is Statistical Analysis?
Statistical analysis, one of the most frequently used analysis methods in financial markets, is conducted to predict future price movements based on historical data. Due to the use of concrete data from historical periods, quantitative analysis comes to the fore in using this type of analysis method.
Almost any meaningful data can be used in the statistical analysis. However, many technical indicators such as moving averages, standard deviation, correlation, volume, etc., which provide various interpretations of price movements, can be shown as favorites in this sense for displaying historical price movements in different forms.
How Does Statistical Analysis Work?
For statistical analysis to be performed correctly, investors need to obtain a meaningful result by accurately examining historical price movements. The stages of the process are Data Collection, Exploratory Data Analysis, Hypothesis Development, Application of Statistical Tests, Model Development, and Model Validation.
Data Collection:
In the data collection phase, various economic indicators such as volume, price, historical trends, GDP, unemployment, and inflation data that may affect price movements for the period under review are gathered.
Exploratory Data Analysis:
In this stage, some meaningful relationships are established between the data obtained in the previous stage. Comparison and correlation results are obtained by considering the extent to which volume or other economic indicators etc. affect price movements. In the Exploratory Data Analysis stage, various methods such as regression analysis, Monte Carlo simulations, and Variance Analysis (ANOVA) are used to determine the relationships between variables.
Hypothesis Development:
The correlation and comparison results obtained after filtering are used to propose various hypotheses about price movements.
Application of Statistical Tests:
The hypotheses obtained in the previous stage are tested on the current market dynamics and inferences are made about the hypothesis’s functionality levels.
Model Development:
Following the testing, modeling studies are carried out to enable the use of hypotheses that confirm that price movements have changed as predicted on future price movements.
Model Validation:
In the final stage, the models developed are integrated into the markets over time and their correctness is verified.
What is the Importance of Statistical Analysis?
Statistical analysis is a type of analysis that allows all investors who want to make informed investment decisions in financial markets, whether short-term or long-term, to make decisions based more on logic with meaningful data, rather than relying solely on factors such as intuition. Using statistical analysis, investors can gain insights into market trends, risk management, and strategy changes.
Trend:
Historical price movements are crucial considering that they can also affect future periods. Investors conducting statistical analysis can conclude the direction of the market in the future by considering the likelihood of historical price movements and patterns repeating.
Risk Management:
Based on historical price movements, some insights can be obtained into the price movements and market conditions of the financial assets being followed. Therefore, with the right analysis, investors can adjust their investments in line with the desired risk level in the light of predicted price movements.
Strategy Changes:
In line with the results obtained through statistical analysis, investors who conclude that a historical price pattern may repeat can change their strategies to better adapt to market conditions.
How Does Statistical Analysis Contribute to Stock Market Forecasting?
Various statistical methods such as moving averages, standard deviation, correlation, and regression models are used to analyze stock data. These methods can be used to correlate various values of financial assets of interest in stock markets, such as volume and price, as well as to determine which patterns (Trend, Cyclical, or Seasonal) they follow. In this way, investors can better determine the conditions under which the next movements of the stocks of interest may be shaped.
On the other hand, ARIMA (AutoRegressive Integrated Moving Average) and various other machine learning methods that use past price movements to predict future price movements also contribute to stock market forecasting. Through machine learning methods, more than two sets of data can be fitted on the same base and a logical linear line of detection can be established.
What are the Statistical Methods Used in Analyzing Stock Market Data?
Various methods and indicators such as moving averages, standard deviations, and correlations are used to analyze the data obtained from stock markets.
Moving averages
, as one of the most widely used indicators in stock markets, are prominent in determining the trending direction of current financial assets.
Standard deviation
is a tool used to measure volatility through past price movements. With this method, investors can make a judgment about the volatility of financial assets in the future.
Correlations
can be used to determine whether there is a direct relationship between multiple variables. The results obtained through correlation are expressed as +1 and -1. While +1 indicates a positive correlation between two variables where they move together, -1 indicates a negative correlation where the variables do not move together.
What are the Types of Statistical Analysis?
Despite many different types of statistical analysis, the most prominent ones are descriptive statistics and inferential statistics.
Descriptive statistics
, one of the methods used in statistical analysis in stock markets, is a summary of past market movements. Through descriptive statistics, numerical data are compiled, aggregated, and summarized. With the inferences made in this way, the mean (average), median (middle value), and mode (most frequently occurring value) values of the data can be determined or data that contribute to volatility measurements such as standard deviation can be found.
Inferential statistics
, on the other hand, is a type of statistical analysis that uses samples obtained from existing data sets to make predictions and forecasts in broader areas such as stock markets. Through inferential statistics, future price flows in financial markets can be determined by using hypotheses formed with the available data. In addition, methods such as regression analysis, which play a role in determining the relationship between different variables, are also included in the scope of inferential statistics.
What are the Advantages of Statistical Analysis for the Stock Market?
Developing and Testing Investment Strategies:
In all financial markets, especially in the stock markets, investors and short-term traders aim to develop appropriate investment strategies. By using statistical analysis, the investment strategy to be used can be decided by revealing the price momentum of the relevant asset with historical data for the financial asset of interest.
Predict Price Movements:
Despite having no definitive results, historical price movements and the patterns shown by stocks are seen to affect current and future price movements. Considering that the investment models revealed through statistical analysis consist of the historical data of the relevant stock, various predictions can be made regarding future price movements.
Correct Interpretation of Economic Data:
GDP, unemployment, and inflation data, indispensable elements of financial investments and affecting the market price movements of the country and companies, are analyzed through statistical analysis. In this way, the price changes of the relevant macroeconomic developments on stocks can be better analyzed.
Portfolio Allocation:
The prediction of future price movements through statistical analysis also affects the risk thresholds of investors. If the price movements detected in some stocks as a result of the analysis constitute more risk than desired, investors may want to change the allocation of stocks in their portfolios.
What are the Disadvantages of Using Statistical Analysis in The Stock Market?
Inconclusive Results:
While most scenarios assume that historical price movements influence current and future price movements, no guarantee can ever be made in this regard. Constantly changing market dynamics may cause various patterns that have occurred on stocks in the past to not be repeated in the future.
Sudden Changes in the Market:
Numerous unpredictable factors such as war, economic crisis, political tensions, etc. can affect stock markets. Such situations, which the market can react to instantaneously, may not coincide with the previously predicted price movements, causing the investment strategy chosen not to work.
Misinterpretation of Data:
Considering that statistical analysis is based on historical data on stocks, it should be taken into account that various incorrect/incomplete data interpretations made by investors on historical data may lead to an incorrect analysis, which can cause incorrect investment strategies to be applied.
Insufficient Data:
Attempting to obtain the volume, price, etc. elements expected to be collected from many different periods through financial assets that do not have sufficient data such as new stocks may lead to incorrect statistical analysis. In this context, it is expected that the asset for which statistical analysis is to be performed should have sufficient financial data from historical periods.
What Role Does Statistical Analysis in Risk Management and Portfolio Optimization?
Informed investors organize the financial assets in their portfolios according to their risk thresholds and their anticipated future price movements. In this context, making an accurate prediction of future price movements using historical and current data is of greater importance. Statistical data analysis, meanwhile, provides investors with valuable insights into future price movements through the historical data it brings together and the machine learning methods used to analyze this data.
In addition to emotional factors such as intuition, analyses based on statistical data support investors’ decision-making processes regarding price movements. In this context, investors can adjust their risk ratios to their desired level in cases where market-wide volatility increases are predicted, etc. On the other hand, making similar predictions only for certain financial assets may lead to changes in portfolio allocations.
FAQ
Is it possible to reach definitive conclusions with the findings obtained through statistical analysis?
No. Although the results obtained through statistical analysis are based on historical financial data rather than speculation, there is no guarantee that they will be 100% accurate.
Is the data used in statistical analysis always manually reviewed?
No. Machine learning methods are used to perform statistical analysis by examining factors such as volume, price, and economic developments to achieve better results.
Can non-quantitative data also be analyzed in statistical analysis?
Yes, for example, investors’ mode or news feeds can also be analyzed to provide more accurate results of statistical analysis.
Can statistical analysis be applied to all financial instruments?
Yes. The most basic condition for performing statistical analysis is to verify that the relevant financial asset has historical data to be analyzed.
What are the common methods used to analyze statistical analysis in stock markets?
Moving averages, correlations, and regression analysis are the most common methods used by investors to analyze statistical analysis.
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