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In this work, we investigate different techniques to predict the monthly trend direction of the S&P 500 market index. The techniques use a machine learning classifier with technical and macroeconomic indicators as input features. The Support Vector Machine (SVM) classifier was explored in-depth in order to optimize the performance using four different kernels; Linear, Radial Basis Function (RBF), Polynomial, and Quadratic. A result found was the performance of the classifier can be optimized by reducing the number of macroeconomic features needed by 30% using Sequential Feature Selection. Further performance enhancement was achieved by optimizing the RBF kernel and SVM parameters through gridsearch. This resulted in final classification accuracy rates of 62% using technical features alone with gridsearch and 60.4% using macroeconomic features alone using Rankfeatures

by Ng Wai-Leung Anthony. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 83-87). / Abstracts in English and Chinese. / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- LITERATURE REVIEW --- p.5 / Chapter 2.1 --- The Importance of Stock Index and Index Futures --- p.6 / Chapter 2.2 --- Importance of Index Forecasting --- p.6 / Chapter 2.3 --- Reasons for the Lead-Lag Relationship between Stock and Futures Markets --- p.9 / Chapter 2.4 --- Importance of the lead-lag relationship --- p.10 / Chapter 2.5 --- Some Empirical Findings of the Lead-Lag Relationship --- p.10 / Chapter 2.6 --- New Approach to Financial Forecasting - Artificial Neural Network --- p.12 / Chapter 2.7 --- Artificial Neural Network Architecture --- p.14 / Chapter 2.8 --- Evidence on the Employment of ANN in Financial Analysis --- p.20 / Chapter 2.9 --- Hong Kong Securities and Futures Markets --- p.25 / Chapter III. --- GENERAL GUIDELINE IN DESIGNING AN ARTIFICIAL NEURAL NETWORK FORECASTING MODEL --- p.28 / Chapter 3.1 --- Procedure for using Artificial Neural Network --- p.29 / Chapter IV. --- METHODOLOGY --- p.37 / Chapter 4.1 --- ADF Test for Unit Root --- p.38 / Chapter 4.2 --- "Error Correction Model, Error Correction Model with Short- term Dynamics, and ANN Models for Comparisons" --- p.38 / Chapter 4.3 --- Comparison Criteria of Different Models --- p.39 / Chapter 4.4 --- Data Analysis --- p.39 / Chapter 4.5 --- Data Manipulations --- p.41 / Chapter V. --- RESULTS --- p.42 / Chapter 5.1 --- The Resulting Models --- p.42 / Chapter 5.2 --- The Prediction Power among the Models --- p.45 / Chapter 5.3 --- ANN Model of Input Variable Selection Using Contribution Factor --- p.46 / Chapter VI. --- CAUSALITY ANALYSIS --- p.54 / Chapter 6.1 --- Granger Casuality Analysis --- p.55 / Chapter 6.2 --- Results Interpretation --- p.56 / Chapter VII --- CONSISTENCE VALIDATION --- p.61 / Chapter VIII --- ARTIFICIAL NEURAL NETWORK TRADING SYSTEM --- p.67 / Chapter 7.1 --- Trading System Architecture --- p.68 / Chapter 7.2 --- Simulation Runs using the Trading System --- p.77 / Chapter XI. --- CONCLUSIONS AND FUTURE WORKS --- p.79

本研究的主要目的在利用模擬實證研究的方式，探討資訊在我國股市股價形成中所扮演的角色。本研究所定義的資訊均屬最低成本或成本趨近於零的歷史性資訊或隨機亂數資訊，這些資訊包括亂數選擇、加權平均指數之反轉移動，與個股股價反轉移動資訊等。理性預期均衡理論提供了資訊與股價間關係的理論基礎。在理性預期均衡理論中，最基本的觀念是股價與攸關資訊間，若非雜訊，二者應具系統化關係。本研究預期可瞭解我國股市部份資訊效率性的意義，亦可進一步瞭解與歷史性資訊在我國股價形成所扮演的角色。 　　本研究採用模擬式實證研究法，以理性預期理論為基礎，探討隨機亂數資訊與歷史性股價資訊在股價形成過程中所扮演的角色，從而檢定我國股票市場之效率性。為了能評估不同層次之歷史資訊所代表之本質，本研究將採用之資訊分為三層次，分三階段進行。且為了觀察市場多、空頭之差異，又特別分別測試。 　　本研究以266家上市公司為抽樣母體，蒐集自民國83年至民國84年中之股價資料，分多、空頭進行研究，獲致以下結論： 　　一、在不利用任何歷史性資訊（亦即非理性狀態下）以制訂投資決策之情況下，若市場為多頭時期，存在有總累積報酬率擊敗市場且顯著差異之事實，且持有週期愈短，總累積報酬率愈高；而若市場為空頭時期，不論持有週期長短，總累積報酬率皆不能擊敗市場，但以各投資組合而言，仍存在有擊敗市場且顯著差異之事實，以此推論，台灣股票市場屬弱式效率市場之範疇。 　　二、當採用市場發行量加權平均指數之反轉移動為投資買賣點之依據，不論市場是處於多頭或空頭，總累積報酬率皆無法擊敗市場之平均表現。但各個投資組合亦存在有擊敗市場且顯著差異之事實，亦可支持台灣股票市場為弱式效率市場之說法。 　　三、若改以個股股價反轉移動為投資決策參考之指標，在多、空頭時期，且不論就個別投資組合或總累積報酬率來看，皆可輕易獲致超額報酬且顯著差異，暗示我國股市股價形成之過程並不能充分反應此一資訊，據此可推論台灣股票市場為弱式效率市場。 　　四、以市場發行量加權平均指數反轉移動資訊所制訂投資決策之績效不能顯著勝過市場之平均表現看來，台灣股票市場之股票股價形成過程中，深受歷史性加權平均指數移動資訊所影響，以致能大部分反映此部分資訊。而以個股股價反轉移動資訊所制訂投資決策之績效卻能輕易勝過市場且顯著差異看來，我國股市中個別股票股價表現似乎存在重大差異，股價與加權指數間並不皆具有高度正相關，投資人若決策錯誤，可能產生「賺了指數，賠了差價」之情形。 / This study aims to examine the role of information plays in the stock price formation by an application of simulated empirical approach. The results of this study can be used for assessing the appropriateness of Fama's definition of efficient market hypothesis (1970) in the Taiwan stock market. The assessment of information in this study includes random selection, reverse movement of stock index, and reverse movement of an individual stock. The analysis includes 266 samples covering bull/bear markets in the period between 1994 and 1995 on which the findings can be summarized as follows. 　　1. If the portfolio is formed upon random selection, the performance of portfolios can significantly beat the market in the bull market. In particular, the shorter turnover is, the higher return can be gained. There exist examples that the random portfolio can beat the market in the bear market no matter which turnover is selected. 　　2. If the portfolio is formed upon the reverse movement of stock index, even though total cumulative returns cannot beat the market average return in all cases, there exist cases beating the market. 　　3. If the portfolio is formed upon the reverse movement of an individual stock, no matter what the bull or bear market is, the portfolio can significantly beat the market for designated cases or total cumulative returns. 　　Thus, the stock price in the Taiwan stock market can be classified as a weak form market or weaker than a weak form market based on the Fama's definition (1970). Future research can consider how the Fama's definition of efficient market hypothesis can be revised in order to be applicable in the Taiwan stock market.

After a long-time preparation, CSI 300 index futures has made a milestone in the financial market in China in the 16 of April, 2010. In order to know what kind of impact will bring to stock market after the appearance of stock index future, the study discusses volatility and volume separately. On one hand, the study applies Modified Levene and GJR-GARCH as the empirical model, and the result indicates that stock return fluctuation is a short-term phenomenon. However, the result shows that the stock return volatility has no difference in the long-run. Furthermore, it not only reduces the asymmetric return fluctuation from good and bad news cause but improve the information efficiency in the spot market after the introduction of the stock index futures. On the other hand, the study applies multiple regression model and panel model to examine the crowding-out effect and the volume difference after the stock index futures enters the market. First, there is no crowding-out effect in the stock market. Second, both the trading volume of the constituent and non-constituent stocks increase after the introduction of the stock index futures, whereas the level of increasing trading volume of the constituent stocks is larger than non- constituent stocks are.

In the thesis, we apply the gene expression programming (GEP) to training profitable trading strategies. We propose a model which utilizes several historical periods that are highly related to the current template period, and the best trading strategies of the historical periods generate the trading signals. To keep stability of our model, we proposed the trading decision mechanism based on simple majority vote in our model. The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) is selected as our investment target and the trading period starts from 2000/9/14 to 2012/1/17, approximately twelve years. In our experiments, the lengths of our training period are 60, 90, 120, 180, and 270 trading days, respectively. We observe that the model with higher voting threshold usually can make profitable trading decisions. The best cumulative return 236.25\% and the best annualized cumulative return 10.63\% occur when the 180-day training models pairs with available threshold 0.21 and voting threshold 0.88, which are higher than the cumulative return 0.96\% and annualized cumulative return 0.08\% of the buy-and-hold strategy.

When adjusted to sovereign entities, the structural credit risk model assumes a negative (positive) relationship between sovereign CDS spreads and stock prices (volatilities). In theory both markets are supposed to incorporate new information simultaneously. Discrepancies from the theoretical relationship can be exploited by capital structure arbitrageurs. In our thesis we study the intertemporal relationship between sovereign CDS and stock index markets in East Asia during the period of 2007 – 2011. We detect a negative (by and large positive) relationship between the Asian CDS spreads and stock indexes (volatilities). Across the whole region the sovereign CDS market dominates the price discovery process. However, 4 out of 7 Asian countries (Japan, Korea, Malaysia and the Philippines) demonstrate a feedback effect. The stock markets of countries with higher credit spreads (Indonesia, the Philippines and Korea) appear to react more severely at heightened variance in the CDS market. When considered separately for turbulent vs. calm periods, we find that the lead-lag relationship between the Asian sovereign CDS and stock markets is not stable. Apart from that, both markets become more interrelated during periods of increased volatility. The dependency of Asian CDS spreads and stock indexes on the “fear index” detected in the frames of robustness check implies an integration of both markets into the global one. Therefore, while seeking for arbitrage opportunities in the respective Asian markets one should also take into account possible influences of broader global factors.

This thesis aims to provide detailed investigation into the role and functioning of the FTSE-100 stock index futures contract, by examining four interrelated issues. Chapter 1 reviews the literature, demonstrating that stock index futures can increase investor utility by offering hedging and investment opportunities. Further, the price discovery role of futures is discussed. Chapter 2 investigates the risk return relationship for the FTSE-100 contract within a CAPM framework. While CAPM adequately explains returns prior to October 1987, post-crash the contract is riskier and excess returns and a day of the week effect are evident. Chapter 3 examines the impact of futures on the underlying spot market using GARCH, which allows examination of the link between information and volatility. While spot prices are more volatile post-futures, this is due to more rapid impounding of information. The view that futures destabilise spot markets and should be subject to further regulation is questioned. Chapter 4 examines futures market efficiency using the Johansen cointegration procedure and variance bounds tests which are developed here. Results suggest futures prices provide unbiased predictions of future spot prices for 1, 2 and 4 months prior to maturity of the contract. For 3, 5 and 6 months prior to maturity the unbiasedness hypothesis does not hold. Chapter 5 discusses the major role of futures; hedging. Hedge ratios and hedging effectiveness are examined in relation to duration and expiration effects. Hedge ratio stability is also examined. Finally, hedging strategies based on historical information are examined. Results show there are duration and expiration effect, hedge ratios are stationary and using historical information does not greatly reduce hedging effectiveness. The FTSE-100 contract is shown to be a highly effective means by which to hedge risk. Chapter 6 provides a summary and concluding remarks concerning the relevance of the research carried out here.

Interest in the results of important macroeconomic information is in relation to the financial crisis deeper than usual. The main objective of this thesis is to evaluate the behavior of individual players on the market during the time of publication of these reports. It uses the standard tools that are available in commercial platforms. Literature, which addresses this area of trading, is not widely available. I dare to say that this thesis offer to the reader non-traditional view on this issue.

The discussion about the corporate sustainability issues getting more importance in recent days. The stock markets around the world also are affected with this subject of discussion. Investors as well as the companies theirselves are considering sustainability concepts during taking their investment decision strategies. Many index providers launched different ’Sustainable stock’ indices around the world to recognise these new investment decision choices. But do the investors actually care about the sustainability during their investment choices? The purpose of the study was to explore the answer of this unsolved question. Towards achieving the goal the study has been conducted to explore the relationships between the announcement of the ’Dow Jones sustainability Index Emerging Markets’ and the market reactions. Though there were some efforts, which were done to understand the patterns of the relationships between these two variables in developed markets, but no such study has been conducted in case of emerging markets. An event study was conducted to find out the answer of the research question. Five years panel data from 2015 to 2019 of the listed companies on the ’Dow Jones sustainability Index Emerging Markets’ were considered as the study sample to analyse the market reactions for the announcement of this index of this study period. The findings of the study did not recieve any significant influences of the announcement of inclusion, exclusion and continuation events on the stock market return at that study period. That means the investors in the emerging markets did not care about the sustainable performance of the listed companies during their investment decisions. But this study provide a deep insight about the future trend of sustainable investment, as the announcement events had some non-significant influences on the return trend. The results of the study indicate that investors are getting aware of the corporate sustainable performances day by day. Hopefully these insights give us the opportunity to anticipate that the investors will consider the sustainability issues more in their investment decision making process in the future.