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Ph.D. thesis of Cristian Păuna
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After several attempts to publish my Ph.D. thesis with different prestigious publishers, I have decided to make this work public and free of charge for anyone. Enjoy! Cristian Păuna
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Capital investment is a sustained activity nowadays. The buy and sell decisions are usually made in technical analysis using the price quote evolution in time. Another useful information provided by any stock exchange is the trading volume for each time interval. The volume information is usually hard to be included in a trading or investment strategy, having an unstable and discontinued evolution in time. Some obsolete ideas indicate a favorable entry period after a maximal traded volume value interval, but today, on the high price volatility markets, when a maximal value is detected, usually is too late for a convenient price entry on that market. This paper presents a mathematical model specially designed for fast and instant market entry decisions based only on the traded volume information. It was found that even the traded volume variation in time is discontinued, a cyclical phenomenon is present in all markets. With the proper mathematical method, the Volume Cyclicality function can be computed in real-time in order to build reliable capital investment signals. The model presented in this paper fills an essential gap in the literature, and it was tested for more than ten years on the most important stock exchanges in the world. Investment results are also included in this paper to prove the efficiency and utility of the presented method. The Volume Cyclicality function is an exclusively mathematical model, and it can be applied in any automated investment software system to improve capital efficiency.
Capital investment is a sustained activity nowadays. After the worldwide release of the electronic trading systems, automated decision-making investment software is the new trend in financial speculation. A significant part of capital trading is fully computerized today. The buying and selling orders are made and sent automatically, almost in real-time. The price evolution is analyzed by servers using advanced mathematical algorithms. This paper will present one of these models named Price Probability Predictor. It is a method to build a probability field based on the price history and the real-time price action. The revealed function will generate the current probability of a price growth in the next time intervals. Automated entry and exit signals and market limit conditions will be built using the new indicator, in order to automate the whole investment process. Capital investment results will also be included in the current paper to qualify the presented trading methodology and to compare it with other similar models. In conclusion, it was found that the Price Probability Predictor is a reliable mathematical algorithm that can assist any trading decisions, in both ways, manual or automatic capital investments.
It is well-known that the receipt of information by any person undergoes transformations, modifications and distortions depending on the personality, the experience, the level of education and culture and the habits of each person. In the activity of promoting and selling a computer service for investors, one can notice that there are several behavioural patterns. Thus, while for some clients the presentation of a capital evolution chart is a decisive factor, other investors can make a decision only after analysing the figures in the investment plan, and some others only after hearing a verbal presentation of the respective service. After a significant number of observations in this area, we were able to develop a collection of investors' behavioural patterns. The present paper presents all these psychological types of behaviour, in the investment context, accompanied by observations on the particularities of each pattern, the identification method, the appropriate manner of transmitting information and, perhaps most importantly, the elements that can positively influence the investor's decision, depending on the typology they belong to. As we are talking about an investment activity, which of course involves taking a risk, the fears that the investor is facing represent a special topic. Poorly addressed in the research literature, investor's fears are the topic of the second part of this paper. We will present you the different types of fear that investors face, the ways in which these can be identified and especially the actions by which these fears can be eliminated or controlled, so that they do not produce negative emotions during the investment. The present paper covers two important topics that are hardly addressed in the research literature. The article addresses equally to both those who work in the field of marketing and promotion of services for investors, as well as to the investors themselves, who can identify themselves as party of different typologies and thus explain their reactions and especially the fears they face. They can also identify the different ways by means of which the investment process can become stress-free.
In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.
After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.
One of the most popular trading methods used in financial markets is the Turtle strategy. Long-time passed since the middle of 1983 when Richard Dennis and Bill Eckhardt disputed about whether great traders were born or made. To decide the matter, they recruited and trained some traders (the Turtles) and give them real accounts and a complete trading strategy to see which idea is right. That was a breakout trading strategy, meaning they bought when the price exceeded the maximum 20 or 50 days value and sold when the price fell below the minimum of the same interval. Since then many changes have occurred in financial markets. Electronic trading was widespread released and financial trading has become accessible to everyone. Algorithmic trading became the significant part of the trading decision systems and high-frequency trading pushed the volatility of the financial markets to new and incredible limits nowadays. The orders are built and sent almost instantly by smart computers using advanced mathematical algorithms. With all these changes there are many questions today regarding the breakouts strategies. Are the Turtle rules still functional? How can the Turtle strategy be automated for algorithmic trading? Are the results comparable with other modern trading strategies? After a short display of the history and the system's rules, this paper will find some answers to all these questions. We will reveal a method to automate a breakout strategy. More different trading strategies originating from the Turtle rules will be presented. A mathematical model to build the trading signals will be described in order to automate the trading process. It was found that all of these rules have a positive expectancy when they are combined with modern limit conditions. The paper will also include trading results obtained with the methods presented in order to compare and to analyze this capital investment methodology adapted especially for algorithmic trading.