ResearchPad - financial-markets https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Fear and stock price bubbles]]> https://www.researchpad.co/article/elastic_article_13818 I evaluate Alan Greenspan’s claim that stock price bubbles build up in periods of euphoria and tend to burst due to increasing fear. Indeed, there is evidence that e.g. during a crisis, triggered by increasing fear, both qualitative and quantitative measures of risk aversion increase substantially. It is argued that fear is a potential mechanism underlying financial decisions and drives the countercyclical risk aversion. Inspired by this evidence, I construct an euphoria/fear index, which is based on an economic model of time varying risk aversion. Based on US industry returns 1959–2014, my findings suggest that (1) Greenspan is correct in that the price run-up initially occurs in periods of euphoria followed by a crash due to increasing fear; (2) on average already roughly a year before an industry is crashing, euphoria is turning into fear, while the market is still bullish; (3) there is no particular euphoria-fear-pattern for price-runs in industries that do not subsequently crash. I interpret the evidence in favor of Greenspan, who was labeled “Mr. Bubble” by the New York Times, and who was accused to be a serial bubble blower.

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<![CDATA[Modeling financial interval time series]]> https://www.researchpad.co/article/5c6f148ad5eed0c48467a270

In financial economics, a large number of models are developed based on the daily closing price. When using only the daily closing price to model the time series, we may discard valuable intra-daily information, such as maximum and minimum prices. In this study, we propose an interval time series model, including the daily maximum, minimum, and closing prices, and then apply the proposed model to forecast the entire interval. The likelihood function and the corresponding maximum likelihood estimates (MLEs) are obtained by stochastic differential equation and the Girsanov theorem. To capture the heteroscedasticity of volatility, we consider a stochastic volatility model. The efficiency of the proposed estimators is illustrated by a simulation study. Finally, based on real data for S&P 500 index, the proposed method outperforms several alternatives in terms of the accurate forecast.

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<![CDATA[How does capital structure change product-market competitiveness? Evidence from Chinese firms]]> https://www.researchpad.co/article/5c63394fd5eed0c484ae6483

Finance research shows capital structure has an important effect on the product-market competitiveness of firms. Our paper documents an asymmetric effect of capital structure on firms’ competitiveness in a sample of Chinese firms. Firms whose capital structure is characterized by a low leverage but rapid leverage growth has a dominant position in their product market. The industry average leverage ratio is also a critical factor influencing firms’ competitiveness. High debt levels hinder firms’ competitiveness. The influence of capital structure on firms’ product-market competitiveness varies based on the extent of industry concentration. In highly concentrated industries, high leverage level and slow leverage growth suppress firms’ competitiveness to a larger extent compared with industries with low concentration.

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<![CDATA[Intervention on default contagion under partial information in a financial network]]> https://www.researchpad.co/article/5c478c36d5eed0c484bd0d24

We study the optimal interventions of a regulator (a central bank or government) on the illiquidity default contagion process in a large, heterogeneous, unsecured interbank lending market. The regulator has only partial information on the interbank connections and aims to minimize the fraction of final defaults with minimal interventions. We derive the analytical results of the asymptotic optimal intervention policy and the asymptotic magnitude of default contagion in terms of the network characteristics. We extend the results of Amini, Cont and Minca’s work to incorporate interventions and adopt the dynamics of Amini, Minca and Sulem’s model to build heterogeneous networks with degree sequences and initial equity levels drawn from arbitrary distributions. Our results generate insights that the optimal intervention policy is “monotonic” in terms of the intervention cost, the closeness to invulnerability and connectivity. The regulator should prioritize interventions on banks that are systematically important or close to invulnerability. Moreover, the regulator should keep intervening on a bank once having intervened on it. Our simulation results show a good agreement with the theoretical results.

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<![CDATA[Ecology of trading strategies in a forex market for limit and market orders]]> https://www.researchpad.co/article/5c2151c2d5eed0c4843fbc76

There is a growing interest to understand financial markets as ecological systems, where the variety of trading strategies correspond to that of biological species. For this purpose, transaction data for individual traders are studied recently as empirical analyses. However, there are few empirical studies addressing how traders submit limit and market order at the level of individual traders. Since limit and market orders are key ingredients finally leading to transactions, it would be necessary to understand what kind of strategies are actually employed among traders before making transactions. Here we demonstrate the variety of limit-order and market-order strategies and show their roles in the financial markets from an ecological perspective. We find these trading strategies can be well-characterized by their response pattern to historical price changes. By applying a clustering analysis, we provide an overall picture of trading strategies as an ecological matrix, illustrating that liquidity consumers are likely to exhibit high trading performances compared with liquidity providers. Furthermore, we reveal both high-frequency traders (HFTs) and low-frequency traders (LFTs) exhibit high trading performance, despite the difference in their trading styles; HFTs attempt to maximize their trading efficiency by reducing risk, whereas LFTs make their profit by taking risk.

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<![CDATA[Predicting altcoin returns using social media]]> https://www.researchpad.co/article/5c1028cad5eed0c484248176

Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called “altcoins” often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.

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<![CDATA[Asymmetrical effects of real exchange rate on the money demand in Saudi Arabia: A non-linear ARDL approach]]> https://www.researchpad.co/article/5c23ffded5eed0c484094266

This present research investigates the money demand function of Saudi Arabia using a long period 1968–2016. In addition, the asymmetrical effects of real exchange rate changes have also been explored in the estimated money demand function. Our empirical results suggest that income and inflation have positive and negative effects on money demand respectively. Further, a real appreciation of US dollar has a positive effect but a real depreciation has a negative effect on the money demand. Furthermore, income and price homogeneity hypotheses do not hold for the estimated elasticities. Moreover, the estimated model is found stable with the theoretically expected effects of money demand’s determinants. Therefore, we are suggesting money supply as a monetary policy instrument to the economy of Saudi Arabia.

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<![CDATA[Improving forecasting accuracy for stock market data using EMD-HW bagging]]> https://www.researchpad.co/article/5b600f8a463d7e3af00e5a8f

Many researchers documented that the stock market data are nonstationary and nonlinear time series data. In this study, we use EMD-HW bagging method for nonstationary and nonlinear time series forecasting. The EMD-HW bagging method is based on the empirical mode decomposition (EMD), the moving block bootstrap and the Holt-Winter. The stock market time series of six countries are used to compare EMD-HW bagging method. This comparison is based on five forecasting error measurements. The comparison shows that the forecasting results of EMD-HW bagging are more accurate than the forecasting results of the fourteen selected methods.

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<![CDATA[Stackelberg Game of Buyback Policy in Supply Chain with a Risk-Averse Retailer and a Risk-Averse Supplier Based on CVaR]]> https://www.researchpad.co/article/5989da4bab0ee8fa60b8cbac

This paper considers a decentralized supply chain in which a single supplier sells a perishable product to a single retailer facing uncertain demand. We assume that the supplier and the retailer are both risk averse and utilize Conditional Value at Risk (CVaR), a risk measure method which is popularized in financial risk management, to estimate their risk attitude. We establish a buyback policy model based on Stackelberg game theory under considering supply chain members' risk preference and get the expressions of the supplier's optimal repurchase price and the retailer's optimal order quantity which are compared with those under risk neutral case. Finally, a numerical example is applied to simulate that model and prove related conclusions.

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<![CDATA[Real-Time Diffusion of Information on Twitter and the Financial Markets]]> https://www.researchpad.co/article/5989d9ddab0ee8fa60b686e8

Do spikes in Twitter chatter about a firm precede unusual stock market trading activity for that firm? If so, Twitter activity may provide useful information about impending financial market activity in real-time. We study the real-time relationship between chatter on Twitter and the stock trading volume of 96 firms listed on the Nasdaq 100, during 193 days of trading in the period from May 21, 2012 to September 18, 2013. We identify observations featuring firm-specific spikes in Twitter activity, and randomly assign each observation to a ten-minute increment matching on the firm and a number of repeating time indicators. We examine the extent that unusual levels of chatter on Twitter about a firm portend an oncoming surge of trading of its stock within the hour, over and above what would normally be expected for the stock for that time of day and day of week. We also compare the findings from our explanatory model to the predictive power of Tweets. Although we find a compelling and potentially informative real-time relationship between Twitter activity and trading volume, our forecasting exercise highlights how difficult it can be to make use of this information for monetary gain.

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<![CDATA[Twitter sentiment around the Earnings Announcement events]]> https://www.researchpad.co/article/5989db50ab0ee8fa60bdbde5

We investigate the relationship between social media, Twitter in particular, and stock market. We provide an in-depth analysis of the Twitter volume and sentiment about the 30 companies in the Dow Jones Industrial Average index, over a period of three years. We focus on Earnings Announcements and show that there is a considerable difference with respect to when the announcements are made: before the market opens or after the market closes. The two different timings of the Earnings Announcements were already investigated in the financial literature, but not yet in the social media. We analyze the differences in terms of the Twitter volumes, cumulative abnormal returns, trade returns, and earnings surprises. We report mixed results. On the one hand, we show that the Twitter sentiment (the collective opinion of the users) on the day of the announcement very well reflects the stock moves on the same day. We demonstrate this by applying the event study methodology, where the polarity of the Earnings Announcements is computed from the Twitter sentiment. Cumulative abnormal returns are high (2–4%) and statistically significant. On the other hand, we find only weak predictive power of the Twitter sentiment one day in advance. It turns out that it is important how to account for the announcements made after the market closes. These after-hours announcements draw high Twitter activity immediately, but volume and price changes in trading are observed only on the next day. On the day before the announcements, the Twitter volume is low, and the sentiment has very weak predictive power. A useful lesson learned is the importance of the proper alignment between the announcements, trading and Twitter data.

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<![CDATA[Understanding Financial Market States Using an Artificial Double Auction Market]]> https://www.researchpad.co/article/5989da05ab0ee8fa60b759a6

The ultimate value of theories describing the fundamental mechanisms behind asset prices in financial systems is reflected in the capacity of such theories to understand these systems. Although the models that explain the various states of financial markets offer substantial evidence from the fields of finance, mathematics, and even physics, previous theories that attempt to address the complexities of financial markets in full have been inadequate. We propose an artificial double auction market as an agent-based model to study the origin of complex states in financial markets by characterizing important parameters with an investment strategy that can cover the dynamics of the financial market. The investment strategies of chartist traders in response to new market information should reduce market stability based on the price fluctuations of risky assets. However, fundamentalist traders strategically submit orders based on fundamental value and, thereby stabilize the market. We construct a continuous double auction market and find that the market is controlled by the proportion of chartists, Pc. We show that mimicking the real state of financial markets, which emerges in real financial systems, is given within the range Pc = 0.40 to Pc = 0.85; however, we show that mimicking the efficient market hypothesis state can be generated with values less than Pc = 0.40. In particular, we observe that mimicking a market collapse state is created with values greater than Pc = 0.85, at which point a liquidity shortage occurs, and the phase transition behavior is described at Pc = 0.85.

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<![CDATA[A detailed heterogeneous agent model for a single asset financial market with trading via an order book]]> https://www.researchpad.co/article/5989db53ab0ee8fa60bdcaa8

We present an agent based model of a single asset financial market that is capable of replicating most of the non-trivial statistical properties observed in real financial markets, generically referred to as stylized facts. In our model agents employ strategies inspired on those used in real markets, and a realistic trade mechanism based on a double auction order book. We study the role of the distinct types of trader on the return statistics: specifically, correlation properties (or lack thereof), volatility clustering, heavy tails, and the degree to which the distribution can be described by a log-normal. Further, by introducing the practice of “profit taking”, our model is also capable of replicating the stylized fact related to an asymmetry in the distribution of losses and gains.

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<![CDATA[Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model]]> https://www.researchpad.co/article/5989db04ab0ee8fa60bc7e6c

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

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<![CDATA[Cross-Correlation Asymmetries and Causal Relationships between Stock and Market Risk]]> https://www.researchpad.co/article/5989da85ab0ee8fa60b9be9c

We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock prices from the Standard & Poor's 500 index for 1994–2013. We focus on the behavior of the cross-correlations at the times of financial crises with significant jumps of market volatility. The observed historical dynamics showed that the dependence between the risks was almost linear during the US stock market downturn of 2002 and after the US housing bubble in 2007, remaining at that level until 2013. Moreover, the averaged cross-correlation function often had an asymmetric shape with respect to zero lag in the periods of high correlation. We develop the analysis by the application of the linear response formalism to study underlying causal relations. The calculated response functions suggest the presence of characteristic regimes near financial crashes, when the volatility of an individual stock follows the market volatility and vice versa.

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<![CDATA[Market Imitation and Win-Stay Lose-Shift Strategies Emerge as Unintended Patterns in Market Direction Guesses]]> https://www.researchpad.co/article/5989da0fab0ee8fa60b78eac

Decisions made in our everyday lives are based on a wide variety of information so it is generally very difficult to assess what are the strategies that guide us. Stock market provides a rich environment to study how people make decisions since responding to market uncertainty needs a constant update of these strategies. For this purpose, we run a lab-in-the-field experiment where volunteers are given a controlled set of financial information -based on real data from worldwide financial indices- and they are required to guess whether the market price would go “up” or “down” in each situation. From the data collected we explore basic statistical traits, behavioural biases and emerging strategies. In particular, we detect unintended patterns of behavior through consistent actions, which can be interpreted as Market Imitation and Win-Stay Lose-Shift emerging strategies, with Market Imitation being the most dominant. We also observe that these strategies are affected by external factors: the expert advice, the lack of information or an information overload reinforce the use of these intuitive strategies, while the probability to follow them significantly decreases when subjects spends more time to make a decision. The cohort analysis shows that women and children are more prone to use such strategies although their performance is not undermined. Our results are of interest for better handling clients expectations of trading companies, to avoid behavioural anomalies in financial analysts decisions and to improve not only the design of markets but also the trading digital interfaces where information is set down. Strategies and behavioural biases observed can also be translated into new agent based modelling or stochastic price dynamics to better understand financial bubbles or the effects of asymmetric risk perception to price drops.

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<![CDATA[Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market]]> https://www.researchpad.co/article/5989db1dab0ee8fa60bce7f7

This study used the dynamic conditional correlations (DCC) method to identify the linkage effects of Chinese stock market, and further detected the influence of network linkage effects on magnitude of security returns across different industries. Applying two physics-derived techniques, the minimum spanning tree and the hierarchical tree, we analyzed the stock interdependence within the network of the China Securities Index (CSI) industry index basket. We observed that that obvious linkage effects existed among stock networks. CII and CCE, CAG and ITH as well as COU, CHA and REI were confirmed as the core nodes in the three different networks respectively. We also investigated the stability of linkage effects by estimating the mean correlations and mean distances, as well as the normalized tree length of these indices. In addition, using the GMM model approach, we found inter-node influence within the stock network had a pronounced effect on stock returns. Our results generally suggested that there appeared to be greater clustering effect among the indexes belonging to related industrial sectors than those of diverse sectors, and network comovement was significantly affected by impactive financial events in the reality. Besides, stocks that were more central within the network of stock market usually had higher returns for compensation because they endured greater exposure to correlation risk.

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<![CDATA[Predicting Market Impact Costs Using Nonparametric Machine Learning Models]]> https://www.researchpad.co/article/5989da6dab0ee8fa60b93b37

Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

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<![CDATA[Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models]]> https://www.researchpad.co/article/5989dab2ab0ee8fa60babe9e

The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis.

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<![CDATA[The Accounting Network: How Financial Institutions React to Systemic Crisis]]> https://www.researchpad.co/article/5989dae6ab0ee8fa60bbd9a2

The role of Network Theory in the study of the financial crisis has been widely spotted in the latest years. It has been shown how the network topology and the dynamics running on top of it can trigger the outbreak of large systemic crisis. Following this methodological perspective we introduce here the Accounting Network, i.e. the network we can extract through vector similarities techniques from companies’ financial statements. We build the Accounting Network on a large database of worldwide banks in the period 2001–2013, covering the onset of the global financial crisis of mid-2007. After a careful data cleaning, we apply a quality check in the construction of the network, introducing a parameter (the Quality Ratio) capable of trading off the size of the sample (coverage) and the representativeness of the financial statements (accuracy). We compute several basic network statistics and check, with the Louvain community detection algorithm, for emerging communities of banks. Remarkably enough sensible regional aggregations show up with the Japanese and the US clusters dominating the community structure, although the presence of a geographically mixed community points to a gradual convergence of banks into similar supranational practices. Finally, a Principal Component Analysis procedure reveals the main economic components that influence communities’ heterogeneity. Even using the most basic vector similarity hypotheses on the composition of the financial statements, the signature of the financial crisis clearly arises across the years around 2008. We finally discuss how the Accounting Networks can be improved to reflect the best practices in the financial statement analysis.

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