nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒04‒15
eight papers chosen by

  1. A Theory of Information overload applied to perfectly efficient financial markets By Giuseppe Pernagallo; Benedetto Torrisi
  2. Interactions between Credit and Market Risk, Diversification vs Compounding effects By Szybisz, Martin Andres
  3. Another Look at Calendar Anomalies By Evanthia Chatzitzisi; Stilianos Fountas; Theodore Panagiotidis
  4. Five Facts About Beliefs and Portfolios By Giglio, Stefano W; Maggiori, Matteo; Ströbel, Johannes; Utkus, Stephen
  5. Feature Engineering for Mid-Price Prediction Forecasting with Deep Learning By Adamantios Ntakaris; Giorgio Mirone; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
  6. SRI: Truths and lies By CANDELON Bertrand,; HASSE Jean-Baptiste,; LAJAUNIE Quentin,
  7. Bitcoin Price Prediction: An ARIMA Approach By Amin Azari
  8. On the Co-movement of Crude, Gold Prices and Stock Index in Indian Market By Abhibasu Sen; Prof. Karabi Dutta Chaudhury

  1. By: Giuseppe Pernagallo; Benedetto Torrisi
    Abstract: Before the massive spread of computer technology, information was far from complex. The development of technology shifted the paradigm: from individuals who faced scarce and costly information to individuals who face massive amounts of information accessible at low costs. Nowadays we are living in the era of big data and investors deal every day with a huge flow of information. In the spirit of the modern idea that economic agents have limited computational capacity, we propose an original model using information overload to show how too much information could cause financial markets to depart from the traditional assumption of informational efficiency. We show that when information tends to infinite, the efficient market hypothesis ceases to be true. This happens also for lower levels of information, when the use of the maximum amount of information is not optimal for investors. The present work can be a stimulus to consider more realistic economic models and it can be further deepened including other realistic features present in financial markets, such as information asymmetry or noise in the transmission of information.
    Date: 2019–04
  2. By: Szybisz, Martin Andres
    Abstract: The relations between credit and market risk have deep roots in financial and economic theory. After a brief theory review, we select five variables and calculate their historical shortfalls. This shortfall is taken as a proxy for market risk quantification. Relating this shortfall to non performing loans as a proxy for credit risk allows us to study the nature of the relation between credit and market risk. The nonlinearity of the relation is discussed in view of diversification and compounding effects.
    Keywords: Credit risk, Market risk, Aggregation, Diversification, Compounding effect
    JEL: D81 E44 G21
    Date: 2019–04–09
  3. By: Evanthia Chatzitzisi (Department of Economics, University of Macedonia); Stilianos Fountas (Department of Economics, University of Macedonia); Theodore Panagiotidis (Department of Economics, University of Macedonia)
    Abstract: We employ daily aggregate and sectoral S&P500 data to shed further light on the day-of-the-week anomaly using GARCH and EGARCH models. We obtain the following results: First, there is strong evidence for day-of-the-week effects in all sectors, implying that these effects are part of a wide phenomenon affecting the entire market structure. Second, using rolling-regressions, we find that significant seasonality represents a small proportion of the total sample. Third, using a logit setup, we examine the impact of four factors, namely recessions, uncertainty, trading volume and bearish sentiment on seasonality. We reveal that recessions and uncertainty have explanatory power for anomalies whereas trading volume does not.
    Keywords: day-of-the-week effect, GARCH, calendar anomalies, S&P500 Index, sectors, rolling regression, logit.
    JEL: C32
    Date: 2019–02
  4. By: Giglio, Stefano W; Maggiori, Matteo; Ströbel, Johannes; Utkus, Stephen
    Abstract: We administer a newly-designed survey to a large panel of retail investors who have substantial wealth invested in financial markets. The survey elicits beliefs that are crucial for macroeconomics and finance, and matches respondents with administrative data on their portfolio composition and their trading activity. We establish five facts in this data: (1) Beliefs are reflected in portfolio allocations. The sensitivity of portfolios to beliefs is small on average, but varies significantly with investor wealth, attention, trading frequency, and confidence. (2) It is hard to predict when investors trade, but conditional on trading, belief changes affect both the direction and the magnitude of trades. (3) Beliefs are mostly characterized by large and persistent individual heterogeneity; demographic characteristics explain only a small part of why some individuals are optimistic and some are pessimistic. (4) Investors who expect higher cash flow growth also expect higher returns and lower long-term price-dividend ratios. (5) Expected returns and the subjective probability of rare disasters are negatively related, both within and across investors. These five facts challenge the rational expectation framework for macro-finance, and provide important guidance for the design of behavioral models.
    Date: 2019–04
  5. By: Adamantios Ntakaris; Giorgio Mirone; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
    Abstract: Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid and illiquid stocks. More specifically, we implement a new set of econometrical features that capture statistical properties of the underlying securities for the task of mid-price prediction. Moreover, we develop a new experimental protocol for online learning that treats the task as a multi-objective optimization problem and predicts i) the direction of the next price movement and ii) the number of order book events that occur until the change takes place. In order to predict the mid-price movement, the features are fed into nine different deep learning models based on multi-layer perceptrons (MLP), convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks. The performance of the proposed method is then evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US and Nordic stocks, respectively. For some stocks, results suggest that the correct choice of a feature set and a model can lead to the successful prediction of how long it takes to have a stock price movement.
    Date: 2019–04
  6. By: CANDELON Bertrand, (CORE, UCLouvain); HASSE Jean-Baptiste, (Aix-Marseille School of Economics); LAJAUNIE Quentin, (Université Paris-Dauphine)
    Abstract: This paper proposes a skeptical look at the socially responsible investing (SRI) industry. Building upon a new database for European and American domestic equity mutual funds, it investigates whether there is a discrepancy between what is said (e.g., names or labels) and what is done (investments of mutual funds holdings) about SRI. It turns out that the correspondence between de jure and de facto SRI is weak. Additionally, using a novel nonlinear factor-augmented panel model, it is found that the de facto ethical positioning only matters for the funds’ financial performance. Both results shed new light on the SRI industry and pave the way for a new regulation framework.
    Keywords: socially responsible investing (SRI); environmental, social and governance (ESG) criteria; ethical mutual funds; performane measurement
    JEL: G11 G14 G23
    Date: 2018–12–07
  7. By: Amin Azari
    Abstract: Bitcoin is considered the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-crypto-economic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA) model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction, e.g. 1-day. On the other hand, when we try to train the ARIMA model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model, trained over a large dataset, and a limited test window of the bitcoin price, with length $w$, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of ($p,q,d$), and window size $w$.
    Date: 2019–04
  8. By: Abhibasu Sen; Prof. Karabi Dutta Chaudhury
    Abstract: This non-linear relationship in the joint time-frequency domain has been studied for the Indian National Stock Exchange (NSE) with the international Gold price and WTI Crude Price being converted from Dollar to Indian National Rupee based on that week's closing exchange rate. Though a good correlation was obtained during some period, but as a whole no such cointegration relation can be found out. Using the \textit{Discrete Wavelet Analysis}, the data was decomposed and the presence of Granger Causal relations was tested. Unfortunately no significant relationships are being found. We then studied the \textit{Wavelet Coherence} of the two pairs viz. NSE-Nifty \& Gold and NSE-Nifty \& Crude. For different frequencies, the coherence between the pairs have been studied. At lower frequencies, some relatively good coherence have been found. In this paper, we report for the first time the co-movements between Crude Oil, Gold and Indian Stock Market Index using Wavelet Analysis (both Discrete and Continuous), a technique which is most sophisticated and recent in market analysis. Thus for long term traders they can include gold and/or crude in their portfolio along with NSE-Nifty index in order to decrease the risk(volatility) of the portfolio for Indian Market. But for short term traders, it will not be effective, not to include all the three in their portfolio.
    Date: 2019–04

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.