Applying Financial Engineering Physics and Market Profile Theory to the Analysis of the Behavior in Future Markets
|關鍵字:||金融工程物理學;市場輪廓;類神經網路;台灣指數期貨;美國道瓊工業指數;Financial Engineering Physics;Market Profile;Neural Network;TAIEX Futures;Dow Jones Industrial Average|
"Being able to identify the market trend and make profit" is the one and only goal that every investor is looking for. However, until today, there is still not a perfect theory that can lead us to making the most accurate prediction on market trend. Many experts tried to predict the market based on researches in statistics, investment, accounting, financial engineering, and others, but most of the predictions were made with ideal assumptions which were not practical in real market. Moreover, along with the commercial globalization and trade liberalization, international capital flows and investments have become more frequent than ever, and therefore, the correlation between financial markets has become increasingly significant. Nowadays the information we get from the markets is diverse, thus, our first priority is to identify which information really matters; filter out the irrelevant as much as possible and find the critical and practical information that can be used. According to Steidlmayer's market profile theory in stock trading, there are new buyers, old buyers, major buyers, individual buyers, offensive and defensive buyers and many other different participants; and among these participants, analysis and prediction on offensive and defensive behaviors are the most difficult ones to make. Despite its difficulty for analysis, offensive and defensive behaviors are often the most critical factors that make the market moves dynamically, and most of the successful investments are made based on making the right analysis and prediction on the trend driven by those factors. Therefore, by using the data of Taiwan Futures Exchange (TAIFEX) and US stock exchanges, this research is to build a prediction model by collaborating financial engineering theories with the Backpropagation Neural Network, and use this prediction model to make analysis on the market trend and the participant's behaviors. First, market profile theory clearly describes the market behavior in a certain period. Then, by using financial engineering physics calculations, we can get a more detailed understanding on how the dynamics has been shifting in the market. Lastly, we let the Backpropagation Neural Network to learn from different market trends. Our expectation is to get more accurate analysis on market trend, and increase the prediction model's accuracy and profitability. In the first phase of the experiment, the result showed that adding factors like market profile price deviation and US markets profile rotation factors was feasible to effectively increase the profitability and the accuracy on prediction; and by comparing the results of the experiments in different interval, we found the market profile theory had better predictability and profitability in the long term prediction. In the second phase of the experiment, we applied cluster analysis into our AI prediction model to identify market trends which were driven by offensive and defensive trading behaviors, and predict such market's future movement based on the analysis. The result showed that adding the mentioned application of cluster analysis into our prediction model could significantly increase the model's prediction accuracy and profitability.
|Appears in Collections:||Thesis|