MISMIS – A comprehensive decision support system for stock market investment

Nowadays, stock market is becoming a popular investment platform for both institutional and individual investors. The current financial information systems serve to provide latest information. However, they lack sophisticated analytical tools. This paper proposes a new architecture for financial information systems. The developed prototype is entitled as the Multi-level and Interactive Stock Market Investment System (MISMIS). It is specially designed for investors to build their financial models to forecast stock price and index. The performance of the financial models can be evaluated on a virtual trading platform. There are other features in MISMIS that are tailor-made to handle financial data; these include synchronized time frame, time series prediction techniques, preprocessing and transformation functions, multi-level modeling and interactive user interface. To illustrate the capability of MISMIS, we have evaluated strategies of trading the future options of Hang Seng Index (HSI). We find that historical HSI, Dow Jones Index, property price index, retailing sales figure, prime lending rate, and consumer price index in Hong Kong are essential factors affecting the performance of the trading of HSI’s future option. Also there are some feedbacks from the in-depth interviews of six financial consultant upon how they perceived the prototype MISMIS.     

Realized volatility of index constituent stocks in Hong Kong

High-frequency financial data are useful for studying the statistical properties of asset returns at lower frequencies, and they have been widely used to study various market microstructure related issues. However, most studies to date have been concentrated on markets in developed economies such as the stock markets in US or UK. This article aims to investigate the statistical properties of stock return volatility in Hong Kong. Using the sample of constituent stocks of Hang Seng Index (HSI) and Hang Seng China Enterprises Index (HSCEI or “H-shares Index”), we found that the mean daily realized volatilities of HSCEI stocks to be significantly higher than their HSI counterpart, while the correlations between H-shares stay relatively lower than that of HSI stocks. A long-memory effect is also reported for the logarithmic standard deviations of all shares, with most of them showing slow decay over the series.     

An adaptive modeling and asset allocation approach to financial markets based on discrete microstructure model

In previous works, it was verified that the discrete-time microstructure (DTMS) model, which is estimated by training dataset of a financial time series, may be effectively applied to asset allocation control on the following test data. However, if the length of test dataset is too long, prediction capability of the estimated DTMS model may gradually decline due to behavior change of financial market, so that the asset allocation result may become worse on the latter part of test data. To overcome the drawback, this paper presents a semi-on-line adaptive modeling and trading approach to financial time series based on the DTMS model and using a receding horizon optimization procedure. First, a long-interval identification window is selected, and the dataset on the identification window is used to estimate a DTMS model, which will be used to do asset allocation on the following short-term trading interval that is referred to as the trading window. After asset allocation is over on the trading window, the length-fixed identification window is then moved to a new window that includes the previous trading window, and a new DTMS model is estimated by using the dataset on the new identification window. Next, asset allocation continues on the next trading window that follows the previous trading window, and then the modeling and asset allocation process will go on according to the above steps. In order to enhance the flexibility and adaptability of the DTMS model, a comprehensive parameter optimization method is proposed, which incorporates particle swarm optimization (PSO) with Kalman filter and maximum likelihood method for estimating the states and parameters of DTMS model. Based on the adaptive DTMS model estimated on each identification window, an adaptive asset allocation control strategy is designed to achieve optimal control of financial assets. The parameters of the asset allocation controller are optimized by the PSO algorithm on each identification window. Case studies on Hang Seng Index (HSI) of Hong Kong stock exchange and S&P 500 index show that the proposed adaptive modeling and trading strategy can obtain much better asset allocation control performance compared with the parameters-fixed DTMS model.     

Modeling Hong Kong’s stock index with the Student t-mixture autoregressive model

It is well known that financial returns are usually not normally distributed, but rather exhibit excess kurtosis. This implies that there is greater probability mass at the tails of the marginal or conditional distribution. Mixture-type time series models are potentially useful for modeling financial returns. However, most of these models make the assumption that the return series in each component is conditionally Gaussian, which may result in underestimates of the occurrence of extreme financial events, such as market crashes. In this paper, we apply the class of Student t-mixture autoregressive (TMAR) models to the return series of the Hong Kong Hang Seng Index. A TMAR model consists of a mixture of g autoregressive components with Student t-error distributions. Several interesting properties make the TMAR process a promising candidate for financial time series modeling. These models are able to capture serial correlations, time-varying means and volatilities, and the shape of the conditional distributions can be time-varied from short- to long-tailed or from unimodal to multi-modal. The use of Student t-distributed errors in each component of the model allows for conditional leptokurtic distribution, which can account for the commonly observed unconditional kurtosis in financial data.     

Effective options trading strategies based on volatility forecasting recruiting investor sentiment

This study investigates an algorithm for an effective option trading strategy based on superior volatility forecasts using actual option price data for the Taiwan stock market. The forecast evaluation supports the significant incremental explanatory power of investor sentiment in the fitting and forecasting of future volatility in relation to its adversarial multiple-factor model, especially the market turnover and volatility index which are referred to as the investors’ mood gauge and proxy for overreaction. After taking into consideration the margin-based transaction cost, the simulated trading indicates that a long or short straddle 15 days before the options’ final settlement day based on the 60-day in-sample-period volatility forecasting recruiting market turnover achieves the best average monthly return of 15.84%. This study bridges the gap between option trading, market volatility, and the signal of the investors’ overreaction through the simulation of the option trading strategy. The trading algorithm based on the volatility forecasting recruiting investor sentiment could be further applied in electronic trading and other artificial intelligence decision support systems.     

Financial volatility trading using recurrent neural networks

We simulate daily trading of straddles on financial indexes. The straddles are traded based on predictions of daily volatility differences in the indexes. The main predictive models studied are recurrent neural nets (RNN). Such applications have often been studied in isolation. However, due to the special character of daily financial time-series, it is difficult to make full use of RNN representational power. Recurrent networks either tend to overestimate noisy data, or behave like finite-memory sources with shallow memory; they hardly beat classical fixed-order Markov models. To overcome data nonstationarity, we use a special technique that combines sophisticated models fitted on a larger data set, with a fixed set of simple-minded symbolic predictors using only recent inputs. Finally, we compare our predictors with the GARCH family of econometric models designed to capture time-dependent volatility structure in financial returns. GARCH models have been used to trade volatility. Experimental results show that while GARCH models cannot generate any significantly positive profit, by careful use of recurrent networks or Markov models, the market makers can generate a statistically significant excess profit, but then there is no reason to prefer RNN over much more simple and straightforward Markov models. We argue that any report containing RNN results on financial tasks should be accompanied by results achieved by simple finite-memory sources combined with simple techniques to fight nonstationarity in the data.     

A Fuzzy Asymmetric GARCH model applied to stock markets

In this paper, we derive a new class of flexible threshold asymmetric Generalized Autoregression Conditional Heteroskedasticity (GARCH) models. We use this tool for analysis and modeling of the properties that are apparent in many financial time series. In general, the transmission of volatility in the stock market is time-varying, nonlinear, and asymmetric with respect to both positive and negative results. Given this fact, we adopt the method of fuzzy logic systems to modify the threshold values for an asymmetric GARCH model. Our simulations use stock market data from the Taiwan weighted index (Taiwan), the Nikkei 225 index (Japan), and the Hang Seng index (Hong Kong) to illustrate the performance of our proposed method. From the simulation results, we have determined that the forecasting of volatility performance is significantly improved if the leverage effect of clustering is considered along with the use of expert knowledge enabled by the GARCH model.     

Volatility Trading ia Temporal Pattern Recognition in Quantised Financial Time Series

We investigate the potential of the analysis of noisy non-stationary time series by quantising it into streams of discrete symbols and applying finite-memory symbolic predictors. Careful quantisation can reduce the noise in the time series to make model estimation more amenable. We apply the quantisation strategy in a realistic setting involving financial forecasting and trading. In particular, using historical data, we simulate the trading of straddles on the financial indexes DAX and FTSE 100 on a daily basis, based on predictions of the daily volatility differences in the underlying indexes. We propose a parametric, data-driven quantisation scheme which transforms temporal patterns in the series of daily volatility changes into grammatical and statistical patterns in the corresponding symbolic streams. As symbolic predictors operating on the quantised streams, we use the classical fixed-order Markov models, variable memory length Markov models and a novel variation of fractal-based predictors, introduced in its original form in Tin_ o and Dorffner [1]. The fractal-based predictors are designed to efficiently use deep memory. We compare the symbolic models with continuous techniques such as time-delay neural networks with continuous and categorical outputs, and GARCH models. Our experiments strongly suggest that the robust information reduction achieved by quantising the real-valued time series is highly beneficial. To deal with non-stationarity in financial daily time series, we propose two techniques that combine ‘sophisticated’ models fitted on the training data with a fixed set of simple-minded symbolic predictors not using older (and potentially misleading) data in the training set. Experimental results show that by quantising the volatility differences and then using symbolic predictive models, market makers can sometimes generate a statistically significant excess profit. We also mention some interesting observations regarding the memory structure in the series of daily volatility differences studied.     

Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition

The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. The presented paper proposes a new approach, based on Intelligent Computation, in particular genetic algorithms, which aims to manage a financial portfolio by using technical analysis indicators (EMA, HMA, ROC, RSI, MACD, TSI, OBV). In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies, such as Buy and Hold and a purely random strategy. The time span (2003–2009) employed to test the approach allowed the performance evaluation under distinct market conditions, culminating with the most recent financial crash. The results are promising since the approach clearly beats the remaining approaches during the recent market crash.     

Forecasting stock returns based on information transmission across global markets using support vector machines

This paper provides evidence that forecasts based on global stock returns transmission yield better returns in day trading, for both developed and emerging stock markets. The study investigates the performance of global stock market price transmission information in forecasting stock prices using support vector regression for six global markets—USA (Dow Jones, S&P500), UK (FTSE-100), India (NSE), Singapore (SGX), Hong Kong (Hang Seng) and China (Shanghai Stock Exchange) over the period 1999–2011. The empirical analysis shows that models with other global market price information outperform forecast models based merely on auto-regressive past lags and technical indicators. Shanghai stock index movement was predicted best by Hang Seng Index opening price (57.69), Hang Seng Index by previous day’s S&P500 closing price (54.34), FTSE by previous day’s S&P500 closing price (57.94), Straits Times Index by previous day’s Dow Jones closing price (54.44), Nifty by HSI opening price (60), S&P500 by STI closing price (55.31) and DJIA by HSI opening price (55.22), and Nifty was found to be the most predictable stock index. Trading using global cues-based forecast model generates greater returns than other models in all the markets. The study provides evidence that stock markets across the globe are integrated and the information on price transmission across markets, including emerging markets, can induce better returns in day trading.     

Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model

Recent developments in multivariate volatility modeling suggest that the conditional correlation matrix can be described by a time series recursion, where the total number of parameters grows by the power-of-two of the dimension of financial returns. The power of two computational requirement makes high-dimensional multivariate volatility modeling very time consuming. In this paper, we propose two simplified specifications in a multivariate autoregressive conditional heteroscedasticity model. The first specification computes an unconditional correlation matrix from standardized residuals of the model. The second specification restricts the sum of the weights in a time-varying conditional correlation equation to be one. Applying a Bayesian sampling scheme allows the number of parameters to be reduced from the power of two of the dimension to the linear order of the dimension only and simultaneously provides us a framework for model comparison. We test our simplified specifications using simulated and real data from three sectoral indices in Hong Kong, three market indices and four exchange rates. The results suggest that our simplified specifications are more effective than the original formulation.     

The role of algorithmic trading systems on stock market efficiency

The rapid development of information technology has changed the dynamics of financial markets. The main purpose of this study is laid on examining the role of IT based stock trading on financial market efficiency. This research specifically focused on algorithmic trading. Algorithmic trading enables investors to trade stocks through a computer program without the need for human interventions. Based on an empirical analysis of the Korean stock market, this study discovered the positive impact of algorithmic trading on stock market efficiency at three-fold. First, the study results indicate that algorithmic trading contributes to the reduction in asymmetric volatility, which causes inefficiency of information in a stock market. Second, an algorithmic trading also increases the operation efficiency of a stock market. Arbitrage trading contributes on the equilibrium between the spot market and futures market as well as on the price discovery. Third, algorithmic trading provides liquidity for market participants contributing to friction free transactions. The research results indicate that stock exchanges based on electronic communications networks (ECNs) without human intervention could augment a financial market quality by increasing trading share volumes and market efficiency so that it can eventually contribute to the welfare of market investors.     

Correlation and hierarchies in financial markets

As an emerging financial market, the trading value of carbon emission trading market has definitely increased in recent years. The carbon emission is not only trading in carbon emitters but also has become an important investment target. To determine the mechanism of this growing market, we analyzed the EU allowances (EUA) price series in European Climate Exchange (ECX) that is the leading European emissions futures market. As other financial market, the absolute value of price change (volatility) in carbon emission trading market also shows long-term power-law correlations. Our analysis shows that definite cross correlations exist between EUA and many other markets. These cross correlations exist in wild-range fields, stock market index, futures of crude, sugar, cocoa, etc., suggesting that in this new carbon emission trading market the speculation behavior had already become a main factor that can affect the price change.     

Forecasting the volatility of a combined multi‐country stock index using GWMA algorithms

Globalization has increased the volatility of international financial transactions, particularly those related to international stock markets. An increase in the volatility of one country's stock market spreads throughout the globe, affecting other countries' stock markets. In particular, the Dow Jones Industrial Average plays an extremely important role in the international stock market. This paper uses the generally weighted moving average method and data from the Dow Jones Industrial Average, the National Association of Securities Dealers Automated Quotations, Japan's Nikkei 225, the Korea Composite Stock Price Index, and the Hong Kong Hang Seng Index to predict the performance of the Taiwan Capitalization Weighted Stock Index. This paper attempts to find the smallest prediction error using the optimal combination of generally weighted moving average model parameters and combinations of various international stock market data and compares the results to that found using the exponentially weighted moving average model to explore differences between the two types of forecasting models.     

Volatility spillovers, interdependence and comovements: A Markov Switching approach

The transmission mechanisms of volatility between markets can be characterized within a new Markov Switching bivariate model where the state of one variable feeds into the transition probability of the state of the other. A number of model restrictions and hypotheses can be tested to stress the role of one market relative to another (spillover, interdependence, comovement, independence, Granger noncausality). The model is estimated on the weekly high-low range of five Asian markets, assuming a central (but not necessarily dominant) role for Hong Kong. The results show plausible market characterizations over the long run with a spillover from Hong Kong to Korea and Thailand, interdependence with Malaysia and comovement with Singapore.     

Forecasting IBEX-35 moves using support vector machines

This research aims at examining the application of support vector machines (SVMs) to the task of forecasting the weekly change in the Madrid IBEX-35 stock index. The data cover the period between 10/18/1990 and 10/29/2010. A trading simulation is implemented so that statistical efficiency is complemented by measures of economic performance. The inputs retained are traditional technical trading rules commonly used in the analysis of equity markets such as the Relative Strength Index (RSI) and the Moving Average Convergence Divergence (MACD) decision rules. The SVMs with given values of the RSI and MACD indicators are used in order to determine the best situations to buy or sell the market. The two outputs of the SVM are both the direction of the market and the probability attached to each forecast market move. The best result that it has been achieved is a hit ratio of 100% using the SVM classifier under some chosen risk-aversion parameters. However, these results are obtained analyzing recent periods rather than using all the dataset information.     

The collocating local volatility framework – a fresh look at efficient pricing with smile

ABSTRACT

It is a market practice to price exotic derivatives, like callable basket options, with the local volatility model [B. Dupire, Pricing with a smile, Risk 7 (1994), pp. 18–20; E. Derman and I. Kani, Stochastic implied trees: Arbitrage pricing with stochastic term and strike structure of volatility, Int. J. Theor. Appl. Finance 1 (1998), pp. 61–110.] which can, contrary to stochastic volatility frameworks, handle multi-dimensionality easily. On the other hand, a well-known limitation of the nonparametric local volatility model is the necessity of a short-stepping simulation, which, in high dimensions, is computationally expensive. In this article, we propose a new local volatility framework called the collocating local volatility (CLV) model which allows for large Monte Carlo steps and therefore it is computationally efficient. The CLV model is by its construction guaranteed to be almost perfectly calibrated to implied volatility smiles/skews at a given set of expiries. Additionally, the framework allows to control forward volatilities without affecting the fit to plain vanillas. The model requires only a fraction of a second for complete calibration to simple vanilla products.     

基于高维状态空间方法构建金融市场网络模型

针对金融市场的核心变量--收益率和波动率，基于高维状态空间模型，利用EM和稀疏算法，分别建立了金融产品之间的收益率网络和波动率网络。前者刻画了金融产品收益之间的相互关系，后者刻画了金融产品风险之间的关系。相对于已有模型，上述模型可有效处理高维时间序列数据。对深圳、上海、香港和纽约市场的股票交易数据分析，找出了相应网络结构特征。以上市场的数据分析结果表明，相对于波动率网络，收益率网络具有更高的度数中心势，把这种现象归因于政策等因素对收益率的影响更为直接和简单，而对波动率的影响则是间接和复杂的。上述研究结果也为构建多变量波动率模型提供参考。     

A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading

The purpose of this article is to present a novel genetic programming trading technique in the task of forecasting the next day returns when trading the EUR/USD exchange rate based on the exchange rates of historical data. Aiming at testing its effectiveness, we benchmark the forecasting performance of our genetic programming implementation with three traditional strategies (naive strategy, MACD, and a buy & hold strategy) plus a hybrid evolutionary artificial neural network approach. The proposed genetic programming technique was found to demonstrate the highest trading performance in terms of annualized return and information ratio when compared to all other strategies which have been used. When more elaborate trading techniques, such as leverage, were combined with the examined models, the genetic programming approach still presented the highest trading performance. To the best of our knowledge, this is the first time that genetic programming is applied in the problem of effectively modeling and trading with the EUR/USD exchange rate. Our application now offers practitioners with an effective and extremely promising set of results when forecasting in the foreign exchange market. The developed genetic programming environment is implemented using the C++ programming language and includes a variation of the genetic programming algorithm with tournament selection.     

Forecasting large scale conditional volatility and covariance using neural network on GPU

Forecasting volatility is an important issue in financial econometric analysis. This paper aims to seek a computationally feasible approach for predicting large scale conditional volatility and covariance of financial time series. In the case of multi-variant time series, the volatility is represented by a Conditional Covariance Matrix (CCM). Traditional models for predicting CCM such as GARCH models are incapable of dealing with high-dimensional cases as there are O(N ²) parameters to be estimated in the case of N-variant asset return, and it is difficult to accelerate the computation of estimating these parameters by utilizing modern multi-core architecture. These GARCH models also have difficulties in modeling non-linear properties. The widely used Restricted Boltzmann Machine (RBM) is an energy-based stochastic recurrent neural network and its extended model, Conditional RBM (CRBM), has shown its capability in modeling high-dimensional time series. In this paper, we first propose a CRBM-based approach to forecast CCM and show how to capture the long memory properties in volatility, and then we implement the proposed model on GPU by using CUDA and CUBLAS. Experiment results indicate that the proposed CRBM-based model obtains better forecasting accuracy for low-dimensional volatility and it also shows great potential in modeling for large-scale cases compared with traditional GARCH models.