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Given that renminbi always breaks the historical high against USD, psychological literature on limited investor attention motivates me to consider whether this eye-grabbing event would have an impact on renminbi trading. Empirical evidence suggests that both nearness to the historical high and hitting the historical high negatively affect renminbi future returns. This result survives from a variety of robustness checks. My findings are consistent with the conservatism theory and suggest that investors tend to under-react in response to the news of breaking the historical high.

This paper is about designing, developing and training a neural network for short-term forecasting of buy-sell trends in foreign exchange markets. We use a set of established financial technical indicators as inputs to the neural network and we develop the architecture to predict a trend and then train the network based on the accuracy of the prediction. We perform extensive real time testing with the closing prices (one per minute) of the USD/EUR exchange rates for a one-year period. The overall approach delivers a system that predicts trends substantially better than individual technical indicators.

Predicting stock direction is challenging as stock price time series are extremely noisy. Moreover, the widely accepted efficient market hypothesis states that it is impossible to consistently generate excess returns than the market over a long-term horizon. Hence, the best approach for investors is thought to be the passive buy-and-hold strategy in indices. However, some researchers suggest that the market does have a predictable component.

This paper’s objective is to provide investors with an alternative predictive system that generates an excess return over the classical buy-and-hold strategy and reduces risk. The authors propose an alternative investing model, Average True Range (ATR) and Momentum-based Long Short-Term Memory online (a-m-LSTM-o), that innovatively uses the technical indicators with Long Short-Term Memory (LSTM). Further, this study experiments with multiple other LSTM investing models using daily indices data of the world’s top five economies.

Based on the prediction, the proposed model’s return is 172%, which is significantly higher than the buy-and-hold return of 139%, and it also has a lower drawdown of −49% compared to −51% for the buy-and-hold strategy. Hence, the authors suggest that the proposed model may be a good alternative to the passive approach of the investors.

We use fuzzy logic engineering tools to detect human behavior in the finance arena, specifically in the technical analysis field. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic system. This system can create an optimum computerized model to evaluate stock price movement. This method relies on human psychology to predict human behavior when certain price movements or certain price formations occur. The success of the system is measured by comparing system output versus stock price movement. The new stock evaluation method is proven to exceed market performance and it can be an excellent tool in the technical analysis field. The flexibility of the system is also demonstrated.

The main purpose of this paper is to investigate the validity and predictability of technical analysis in the Taiwan stock market. Bootstrapped tests of White (2000) and of Hansen (2005) are employed to ascertain whether there exists a superior trading rule among two broadly used sets of technical analysis. One coming from Brock et al. (1992) and the other from Sullivan et al. (1999). Moreover, this study brings together powerful bootstrapped tests along with two institutional adjustments to ascertain the efficacy of technical analysis: (1) non-synchronous trading and (2) transaction costs. The empirical results indicate that this triad-data snooping, non-synchronous trading and transaction costs, has a great impact on the performance of technical analysis. In fact, the Taiwan stock market stands for market efficiency, and economical profits cannot be rendered from technical analysis in this market.

This paper inspects whether variable- and fixed-length moving averages (VMA and FMA), and trading range breakout (TRB) rules have prognostic capability and can earn profits superior to buy-and-hold plan, when applied on KSE-100 index of Pakistan stock market during the full sample period January 1, 1997 to December 31, 2013. Full sample results provided empirical evidence for VMA rule that it has significant predictive power and is able to generate profits superior to simple buy-and-hold plan even after inclusion of transaction costs. The highest mean buy returns yielded by VMA, FMA and TRB rules are seen in noncrises periods. The overall implication of this study is that traders in the Pakistan stock market can utilize this information to obtain excess returns on a regular basis.

Technical analysis helps investors to better time their entry and exit from financial asset positions. This methodology relies solely on past information on financial assets price and volumes to predict a financial asset’s future price trend. Modern research has established that combined with other sentiment measures such as social media, it can outperform the standard buy and hold strategy. Moreover, it has been documented that novice and professional investors technical analysis in their investing strategy. An experienced investor should combine fundamental analysis and technical analysis for better trading results. Programmers use technical analysis to create algorithmic trading systems that learn and adapt to the changing trading environments and perform trading accordingly without human involvement. There are hundreds of technical tools offered by known trading platforms. investors must use specific tools that fit their trading style and risk adoption. Moreover, different financial assets such as stocks, exchange trade funds (ETFs), cryptocurrency, futures, and commodities demand different sets of tools. Furthermore, investors should use these tools according to the time frame they use for trading. This paper will discuss different technical tools that are used to help traders of different time frames and different financial assets to achieve better returns over the traditional buy and hold strategy.

Singular Spectrum Analysis (SSA) is a non-parametric approach that can be used to decompose a time-series as trends, oscillations and noise. Trend-following strategies rely on the principle that financial markets move in trends for an extended period of time. Moving Averages (MAs) are the standard indicator to design such strategies. In this study, SSA is used as an alternative method to enhance trend resolution in comparison with the traditional MA. New trading rules using SSA as indicator are proposed. This paper shows that for the Down Jones Industrial Average (DJIA) and Shangai Securities Composite Index (SSCI) time-series the SSA trading rules provided, in general, better results in comparison to MA trading rules.

In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.

The connection between Singular Spectrum Analysis (SSA) decomposition and short-term market movements is investigated. Since SSA is a non-parametric approach, suitable to decompose general time-series into meaningful components, such as trends, oscillations and noise, it is proposed as a new oscillator-type Technical Indicator, replacing popular ones. New Technical Trading Rules (TTRs) are designed and applied to some major global stock indexes to illustrate the benefits in terms of revealing market movements. The performance is evaluated according to different risk-adjustment metrics and the empirical results reveal that the SSA-TTRs may outperform some popular technical oscillators and also the Buy & Hold strategy.

Chart patterns are one of the important tools used by the financial analysts for predicting future price trends (subsequent trends) in stock markets. Although many works related to the descriptions of chart patterns and several effective methods to identify chart patterns from the financial time series have been proposed in recent years, there is no in-depth study about the general characteristics of the subsequent trends. In this paper, we proposed a general framework for mining subsequent trend for chart patterns. We extensively analyze the characteristics of subsequent trends of chart patterns found with the proposed framework. Based on the analysis, we propose a concept called subsequent trend pattern by mining frequently occurring shapes from these trends. The process of subsequent trend pattern mining was evaluated on a dataset containing 502 time series from S&P 500 and a test dataset containing 494 stocks from Yahoo finance. The proposed concept of subsequent trend pattern provides a solid foundation for the understanding of chart patterns in predicting future price movement and extends the formal definition of chart patterns.

Accurate Value at Risk (VaR) estimations are crucial for the robustness and stability of a financial system. Even though significant advances have been made in the field of risk modeling, many crises have emerged during the same period, and an explanation for this is that the advanced models are not widely applied in the financial industry due to their mathematical complexity. In contrast to the mathematically complex models that torture the data in the output stage, we suggest a new approach that filters the data inputs, based on Technical Analysis (TA) signals. When the trading signals suggest that the conditions are positive (negative) for investments we use data from the previously documented positive (negative) periods in order to calculate the VaR. In this way, we use input data that are more representative of the financial conditions under examination and thus VaR estimations are more accurate and more representative (nonprocyclical) than the conventional models’ estimation that use the last nonfiltered $x$-day observations. Testing our assumptions in the US stock market for the period 2000–2017, the empirical data confirmed our hypothesis. Moreover, we suggest specific legislative adjustments that contribute to more accurate and representative VaR estimations: (i) an extra backtesting procedure at a lower than the 99% confidence level as a procyclicality test and (ii) to ease the minimum requirement of 250 observations that is currently the input threshold because it leads to less accurate VaR estimations.

In this paper, the recently introduced improved moving average methodology in [1] is employed and it is applied in two energy ETFs. It is compared to the standard moving average methodology and the buy and hold strategy. Investors who are interested in energy-related sectors and trade using averages, could benefit by forming their strategies based on this improved moving average methodology as it returns higher profits accompanied by decreased risk (measured in terms of drawdown).

Intraday information about stocks or financial indexes is important for the definition of investors' strategies. In this paper two problems where the intraday information is used are studied: (i) modeling a bandwidth for the range of daily maximum and daily prices/values; (ii) modeling an upper and a lower bounds for the daily maximum and daily prices/values. A non-linear transformation of a modified AR(p) process is used, with the parameters computed by the scatter search metaheuristic. The approach is tested with historical data from NASDAQ, DAX, CAC40 and S&P financial series.

The increasingly massive use of advanced Machine Learning methodologies in the financial field sector has led credit institutions to quickly move to new FinTech technologies. This paper deals with how a battery of Artificial Neural Networks (ANN), dedicated to the automatic recognition of financial patterns of potential interest to traders, can be designed and validated. The battery of neural networks that have been designed is composed of a shallow ANN, a deep ANN with ReLu, a deep ANN with Dropout and a convolutional network (ConvNet). Depending on the type of classification problem, the ANN battery dynamically recognizes the best classifier and makes use of it for pattern recognition. The first part of the paper describes how these technologies work, the second one performs a validation of the code and the third one suggests a technical analysis application on financial time series.

Forecasting assets’ prices is the aim of each trader, although the trading approaches employed may vary a lot. The development of machine learning techniques has brought the opportunity to design mechanic trading systems based on dynamic artificial neural networks. The aim of this paper is to combine traditional technical indicators [such as exponential weighted moving average (EWMA), percentage volume oscillator (PVO) and stochastic indicator — %K and %D] with the nonlinear autoregressive networks (NAR and NARX). The first part of the paper describes how neural networks designed for forecasting time series work, the second one performs a deeper validation of the code and the third one combines the dynamic networks with traditional technical indicators in order to generate reliable mechanic signals. The article ends with a back testing of the trading system performed on Dow Jones Industrial Average and on Nasdaq Composite Indexes.

This paper proposes a novel state-space approach to explain stock market dynamics driven by different types of trading, which leads to a new promising scheme for proactive risk management in financial investment. Particularly, it is assumed that the current price changes are formulated through daily trading by multiple types of traders, each of whom follows a specific investment strategy based on technical indicators and a fuzzy logic using past data of stock prices, volumes and yield curves. Moreover, the current price changes are represented by a linear combination of those multiple trading types, where the coefficients corresponding with the size of impact on the price changes are regarded as time-varying state variables to be sequentially estimated under a state-space framework. Thereby, this work develops a new factor decomposition method on price changes from a perspective of different traders’ demand and supply to analyze the current situations and potential risks in financial markets. In empirical experiments, it is shown that the implementation of particle filtering algorithm makes it possible to replicate market price changes. Further, new signals based on the estimated states are developed, which are applied to proactive risk management in financial investment. Especially, it has been found that the demands of yield curve-based traders subtracting those of trend-followers could be a promising signal of stock market crashes, which has successfully enhanced simple buy-and-hold strategy of SP, as well as constant proportion strategies.

In this paper, we apply an updated Coppock trading rule and four trading strategies to two energy ETFs, United States Oil (USO) and United States Gasoline Fund (UGA), using weekly data from 2006 to 2022. Our four trading strategies are designed for different levels of risk tolerance. Strategy 1 is for low risk tolerance investors, strategy 2 for medium risk tolerance investors, and strategies 3 and 4 are for high-risk tolerance investors. For each ETF, we compare the performance of buying and holding this ETF (B&H strategy) to the performance of our trading rule for that ETF. We find our trading rules significantly outperforms the B&H strategy. Traders with low, medium, and high-risk tolerance can use our trading rule with combination of four recommended strategies and obtain an improved risk-return tradeoff than the B&H strategy. Further, our results are robust when we apply our trading system to two equal sub-periods for each ETF.

This chapter discusses the methods and applications of fundamental analysis and technical analysis. In addition, it investigates the ranking performance of the Value Line and the timing and selectivity of mutual funds. A detailed investigation of technical versus fundamental analysis is first presented. This is followed by an analysis of regression time-series and composite methods for forecasting security rates of return. Value Line ranking methods and their performance then are discussed, leading finally into a study of the classification of mutual funds and the mutual-fund managers’ timing and selectivity ability. In addition, the hedging ability is also briefly discussed. Sharpe measure, Treynor measure, and Jensen measure are defined and analyzed. All of these topics can help improve performance in security analysis and portfolio management.

This study examines how fundamental accounting information can be used to supplement technical information to separate momentum winners from losers. We first introduce a ratio of liquidity buy volume to liquidity sell volume (BOS ratio) to proxy the level of information asymmetry for stocks and show that the BOS momentum strategy can enhance the profits of momentum strategy. We further propose a unified framework, produced by incorporating two fundamental indicators — the FSCORE (Piotroski, 2000) and the GSCORE (Mohanram, 2005) — into momentum strategy. The empirical results show that the combined investment strategy includes stocks with a larger information content that the market cannot reflect in time, and therefore, the combined investment strategy outperforms momentum strategy by generating significantly higher returns.