Abstract: Savings accounts are owned by most households, but little is known about the performance of households’ investments. We create a unique dataset by matching information on individual savings accounts from the DNB Household Survey with market data on account-specific interest rates and characteristics. We document considerable heterogeneity in returns across households, which can be partly explained by financial sophistication. A one-standard deviation increase in financial literacy is associated with a 13% increase compared to the median interest rate. We isolate the usage of modern technology (online accounts) as one channel through which financial literacy has a positive association with returns.

Abstract: Modeling and predicting stock prices is an important and challenging task in the field of financial market. Due to the high volatility of stock prices, traditional data mining methods cannot identify the most relevant and critical market data for predicting stock price trend. This paper proposes a stock price trend predictive model (TPM) based on an encoder-decoder framework that predicting the stock price movement and its duration adaptively. This model consists of two phases, first, a dual feature extraction method based on different time spans is proposed to get more information from the market data. While traditional methods only extract features from information at some specific time points, this proposed model applies the PLR method and CNN to extract the long-term temporal features and the short-term spatial features from market data. Then, in the second phase of the proposed TPM, a dual attention mechanism based encoder-decoder framework is used to select and merge relevant dual features and predict the stock price trend. To evaluate our proposed TPM, we collected high-frequency market data for stock indexes CSI300, SSE 50 and CSI 500, and conducted experiments based on these three data sets. The experimental results show that the proposed TPM outperforms the existing state-of-art methods, including SVR, LSTM, CNN, LSTM_CNN and TPM_NC, in terms of prediction accuracy.

Abstract: This paper presents an advanced market bidding and operation strategy for the joint participation of a solar plant with storage in Energy and secondary reserve markets (SRMs) A linear optimization is applied in order to calculate the optimal day-ahead and intraday market bids through a model predictive control (MPC) approach, considering solar generation forecast, hourly market prices as well as battery acquisition, operating and degradation costs Moreover, several rule-based strategies are selected to participate in secondary reserve markets, focused on more profitable and reliable hours through the estimation of market prices and the expected operation of the battery Finally, the real-time operation tackles all the uncertain parameters, such as the reserve requirements and solar forecast errors, with the objective of achieving an optimal tradeoff between market profits, battery costs and market technical fulfillments Several market strategies have been compared in techno-economic terms with annual real market data Regarding the most advanced strategy, market benefits are improved 642% from base case and non-fulfillment level of SRM is reduced up to 149%

Abstract: I characterize the optimal financial regulation policy in an economy where financial intermediaries trade capital assets on behalf of households, but must retain an equity stake to align incentives. Financial regulation is necessary because intermediaries cannot be excluded from privately trading in capital markets. They don't internalize that high asset prices force everyone to bear more risk. The socially optimal allocation can be implemented with a tax on asset holdings. I derive a sufficient statistic for the externality in terms of observable variables, valid for heterogeneous intermediaries and asset classes, and arbitrary aggregate shocks. I use market data on leverage and volatility of intermediaries' equity to measure the externality, which co-moves with the business cycle.

Abstract: Following the popularity of the concepts of “too big to fail” and “too connected to fail” after the global financial crisis, the concept of “too central to fail” has garnered considerable attention recently. In this study, we suggest a “too central to fail” systemic risk measure, Rank, using the PageRank algorithm. Then, adopting a centrality perspective, we compare this measure, which effectively captures network relationships among financial institutions, with other well-known systemic risk measures, conditional value at risk (CoVaR) and marginal expected shortfall (MES). First, we model a simulation that generates bilateral connections among financial institutions. Second, we use real market data representing United States financial institutions. We show that Rank can capture the network structure among financial institutions better than CoVaR and MES. Further, Rank does not have procyclical properties; therefore, it is not dependent on market conditions. This study contributes to the development of a timely measure using publicly available market data. The measure also overcomes the shortcomings of the balance sheet-based approach, which is subject to time lags, because financial institutions release balance sheets quarterly basis. We also include equity and liability-type assets, in which systemic risks mainly propagate through intricately connected liability obligations. The findings will help regulators and policy-makers understand the implications of monitoring systemic risks from a network perspective.

Abstract: Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock-related events Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem In this work, we study how to explore multiple data sources to improve the performance of the stock prediction We introduce an extended coupled hidden Markov model incorporating the news events with the historical trading data To address the data sparsity issue of news events for each single stock, we further study the fluctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods

Abstract: The purpose of this paper is to model interest rates from observed financial market data through a new approach to the Cox–Ingersoll–Ross (CIR) model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek framework. However, there are a number of issues in describing interest rate dynamics within the CIR framework on which focus should be placed. Therefore, a new methodology has been proposed that allows forecasting future expected interest rates from observed financial market data by preserving the structure of the original CIR model, even with negative interest rates. The performance of the new approach, tested on monthly-recorded interest rates data, provides a good fit to current data for different term structures.,To ensure a fitting close to current interest rates, the innovative step in the proposed procedure consists in partitioning the entire available market data sample, usually showing a mixture of probability distributions of the same type, in a suitable number of sub-sample having a normal/gamma distribution. An appropriate translation of market interest rates to positive values has been introduced to overcome the issue of negative/near-to-zero values. Then, the CIR model parameters have been calibrated to the shifted market interest rates and simulated the expected values of interest rates by a Monte Carlo discretization scheme. We have analysed the empirical performance of the proposed methodology for two different monthly-recorded EUR data samples in a money market and a long-term data set, respectively.,Better results are shown in terms of the root mean square error when a segmentation of the data sample in normally distributed sub-samples is considered. After assessing the accuracy of the proposed procedure, the implemented algorithm was applied to forecast next-month expected interest rates over a historical period of 12 months (fixed window). Through an error analysis, it was observed that our algorithm provides a better fitting of the predicted expected interest rates to market data than the exponentially weighted moving average model. A further confirmation of the efficiency of the proposed algorithm and of the quality of the calibration of the CIR parameters to the observed market interest rates is given by applying the proposed forecasting technique.,This paper has the objective of modelling interest rates from observed financial market data through a new approach to the CIR model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek model (Section 2). However, there are a number of issues in describing short-term interest rate dynamics within the CIR framework on which focus should be placed. A new methodology has been proposed that allows us to forecast future expected short-term interest rates from observed financial market data by preserving the structure of the original CIR model. The performance of the new approach, tested on monthly data, provides a good fit for different term structures. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, clustered volatility and skewed tails), as well as the new ones added by the current market environment (particularly the need to model a downward trend to negative interest rates).

Abstract: Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as optimal power flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices Our methodology uses the latest advancements in statistical learning to cope with high-dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism Rigorous validations using the Southwest power pool market data reveal strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real-time prices The proposed approach demonstrates a remarkable proximity to the state-of-the-art industry benchmark, while assuming a fully decentralized, market-participant perspective Finally, we recognize limitations of the proposed and other evaluated methodologies in predicting large price spike values

Abstract: We provide a framework for empirical analysis of negotiated-price markets. Using mortgage market data and a search and negotiation model, we characterize the welfare impact of search frictions and ...

Abstract: This paper develops a machine learning framework for algorithmic trading with virtual bids in electricity markets. In the proposed algorithmic trading strategy, a budget and risk constrained portfolio optimization problem is solved, which selects the virtual transactions to be executed. In order to maximize the expected net earnings of the virtual bid portfolio, a mixture density network model is developed to provide robust and accurate forecasts for electricity price spread between day-ahead and real-time market. By leveraging a coherent risk measure and historical price samples, the risk-constrained portfolio optimization problem is solved efficiently. Backcasting results based on market data from ISO New England show that our proposed mixture density network based trading strategy consistently outperforms the benchmark online learning approach.

Abstract: In this thesis, we develop a comprehensive account of the expressive power, modelling efficiency, and performance advantages of so-called trading agents (i.e., Deep Soft Recurrent Q-Network (DSRQN) and Mixture of Score Machines (MSM)), based on both traditional system identification (model-based approach) as well as on context-independent agents (model-free approach). The analysis provides conclusive support for the ability of model-free reinforcement learning methods to act as universal trading agents, which are not only capable of reducing the computational and memory complexity (owing to their linear scaling with the size of the universe), but also serve as generalizing strategies across assets and markets, regardless of the trading universe on which they have been trained. The relatively low volume of daily returns in financial market data is addressed via data augmentation (a generative approach) and a choice of pre-training strategies, both of which are validated against current state-of-the-art models. For rigour, a risk-sensitive framework which includes transaction costs is considered, and its performance advantages are demonstrated in a variety of scenarios, from synthetic time-series (sinusoidal, sawtooth and chirp waves), simulated market series (surrogate data based), through to real market data (S\&P 500 and EURO STOXX 50). The analysis and simulations confirm the superiority of universal model-free reinforcement learning agents over current portfolio management model in asset allocation strategies, with the achieved performance advantage of as much as 9.2\% in annualized cumulative returns and 13.4\% in annualized Sharpe Ratio.

Abstract: In this study, we use random forest to predict several cryptocurrencies’ prices by using part of factors in Alpha101 [1] to represent features from the history of cryptocurrencies’ market data on Binance and Bitfinex. The result shows our strategy with some factors from Alpha101 is effective in cryptocurrency trading.

Abstract: I test whether the display format of market data affects the trading performance and behavior of retail investors. To do so, I exploit a large brokerage dataset covering a period during which the market information provided to the broker’s customers changed in format, but not in content. I find that a simultaneous display of cross-stock market data reduces the cognitive cost of monitoring the market and thus helps investors obtain better execution prices. In particular, investors better mitigate non-execution and adverse-selection risks when trading with limit orders. Hence, the display format of market data matters for the individual investor.

Abstract: In this thesis, we develop a comprehensive account of the expressive power, modelling efficiency, and performance advantages of so-called trading agents (i.e., Deep Soft Recurrent Q-Network (DSRQN) and Mixture of Score Machines (MSM)), based on both traditional system identification (model-based approach) as well as on context-independent agents (model-free approach). The analysis provides conclusive support for the ability of model-free reinforcement learning methods to act as universal trading agents, which are not only capable of reducing the computational and memory complexity (owing to their linear scaling with the size of the universe), but also serve as generalizing strategies across assets and markets, regardless of the trading universe on which they have been trained. The relatively low volume of daily returns in financial market data is addressed via data augmentation (a generative approach) and a choice of pre-training strategies, both of which are validated against current state-of-the-art models. For rigour, a risk-sensitive framework which includes transaction costs is considered, and its performance advantages are demonstrated in a variety of scenarios, from synthetic time-series (sinusoidal, sawtooth and chirp waves), simulated market series (surrogate data based), through to real market data (S\&P 500 and EURO STOXX 50). The analysis and simulations confirm the superiority of universal model-free reinforcement learning agents over current portfolio management model in asset allocation strategies, with the achieved performance advantage of as much as 9.2\% in annualized cumulative returns and 13.4\% in annualized Sharpe Ratio.

Abstract: In the finance world inventory trading is one of the most necessary activities. Stock market prediction is an act of attempting to decide the future price of a stock other monetary instrument traded on a financial exchange. The technical and integral or the time sequence evaluation is used with the aid of most of the stockbrokers while making the inventory predictions. This paper explains the prediction of a stock using Machine Learning. The input parameters include -open, high, low, close rate, trading volume, Price to Earning Ratio, MA, MACD for more accuracy. The Machine Learning algorithm, Random Forest Regression has been implemented in Python programming language which is used to predict the stock market. The algorithm has been used on the historical stock data along with web- scraping technique that has been applied to catch current market data of the stock. The recursive training model take its predicted value as input to predict further long term future stock rates.

Abstract: We examine whether a put-call ratio, derived from a unique set of market data, can be used to predict directional moves in asset prices during various market conditions between March 2005 and Decem...

Abstract: Braun (The ecological rationality of historical costs and conservatism. Accounting, Economics and Law: A Convivium, this issue) argues that the traditional accounting principles underlying the revenue-expense approach such as Historical Cost and Conservatism are ecologically rational in that they help organizations survive better in uncertain economic environments. More importantly, Braun argues that the revenue-expense approach generates new private information, which informs markets and makes them more effective (Hayek, 1945, The use of knowledge in society. The American Economic Review, 35(4), 519–530), as opposed to merely reflecting back market data under the asset-liability approach (e.g. Sunder, 2011, IFRS monopoly: The Pied Piper of financial reporting. Accounting and Business Research, 41(3), 291–306). We try to explicate the nature of the new private information generated jointly by Historical Cost and Conservatism, and how this information facilitates the survival of individual entrepreneurs and organizations in market competition.

Abstract: Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity research reports to help investors make decisions. In this paper, we examine analysts' decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts' questions which we correlate with analysts' pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts' post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts' decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.

Abstract: Two-stage electricity market clearing is designed to maintain market efficiency under ideal conditions, e.g., perfect forecast and nonstrategic generation. This work demonstrates that the individual strategic behavior of inelastic load participants in a two-stage settlement electricity market can deteriorate efficiency. Our analysis further implies that virtual bidding can play a role in alleviating this loss of efficiency by mitigating the market power of strategic load participants. We use real-world market data from New York ISO to validate our theory.

Abstract: Quantitative Finance (QF) utilizes increasingly sophisticated mathematic models and advanced computer techniques to predict the movement of global markets, and price the derivatives and other assets. Being able to react quickly and intelligently to fast-changing markets is a decisive success factor for trading companies. To date, the rise of QF requires an integrated toolchain of enabling technologies to carry out complex event processing on the explosive growth and diversified forms of market metadata, in pursuit of a microsecond latency on an Exabyte-level dataset. Inspired by this, we present a data-driven execution paradigm that untangles the dependencies of complex processing events and integrate the paradigm with a big data infrastructure that streams time series data. This integrated platform is termed as the QuantCloud platform. Essentially, QuantCloud executes the complex event processing in a data-driven mode and manages large amounts of diversified market data in a data-parallel mode. To show its practicability and performance, we develop a prototype and benchmark by applying real-world QF research models on the New York Stock Exchange (NYSE) data. Using this prototype, we demonstrate this platform with an application to: (i) data cleaning and aggregating (including the computing of logarithmic returns from tick data and the finding the medians of grouped data) and (ii) data modeling: the autoregressive-moving average (ARMA) model. The performance results show that (a) this platform obtains a high throughput (usually in the order of millions of tick messages per second) and a sub-microsecond latency; (b) it fully executes data-dependent tasks through a data-driven execution; and (c) it implements a modular design approach for rapidly developing these data-crunching methods and QF research models. This platform resulting from an aggregated effort of the data-driven execution and big data infrastructure, offers the financial engineers with new insights and enhanced capabilities for effective and efficient incorporation of big data complex event processing technologies in their workflow.

Abstract: Composing a team of professional players is among the most crucial decisions in association football. Nevertheless, transfer market decisions are often based on myopic objectives and are questionable from a financial point of view. This paper introduces a chance-constrained model to provide analytic support to club managers during transfer windows. The model seeks a top-performing team while adapting to different budgets and financial-risk profiles. In addition, it provides a new rating system that is able to numerically reflect the on-field performance of football players and thus contribute to an objective assessment of football players. The model and rating system are tested on a case study based on real market data. The data from the case study are available online for the benefit of future research.

Abstract: We explore various means of quantifying integration using two of the D4R Challenge datasets. We propose various integration indices and discuss their output. We combine the data from the D4R Challenge with data from the GDELT Project and with data on transactions on the housing market in Turkey. We also describe research directions to be undertaken in the future using the D4R data.

Abstract: We analyze comparative advantages/disadvantages of small and large banks in improving household sentiment regarding financial conditions. We match sentiment data from the University Of Michigan Surveys Of Consumers with local banking market data from 2000 to 2014. Surprisingly, the evidence suggests that large rather than small banks have significant comparative advantages in boosting household sentiment. Findings are robust to instrumental variables and other econometric methods. Additional analyses are consistent with both scale economies and the superior safety of large banks as channels behind the main findings. These channels appear to more than offset stronger relationships with and greater trust in small banks.

Abstract: Purpose: The purpose of this article is to empirically test whether wealthy economies have better accounting quality (AQ) compared to their “poor” counterparts. Design/methodology/approach: To test the formulated hypothesis, this article examines accounting and market data of 40 countries' capital markets, obtained from Compustat Global and Compustat North America, and spanned throughout the last quarter of century, from 1992 to 2016. Country wealth and controlling- and valuation-usefulness of accounting information are proxied by gross domestic product per capita, conditional accounting conservatism and value relevance of earnings and book values, respectively. Findings: Descriptive analysis, consistent with the prior literature, reveals that controlling-usefulness and valuation-usefulness of accounting information significantly negatively correlate with each other, putting them as alternative (rather than compatible) objectives of the accounting system. The major finding shows that wealthy economies report significantly more controlling-useful but about equally valuation-useful accounting information compared to their poor counterparts. Practical implications: The findings are interesting from investors as well as standard setters' perspective. Originality/value: According to Ball (Journal of International Accounting Research (2016), 15(2), 1–6), wealthy economies are likely to invest more in the establishment and development of a country-level reporting infrastructure such as accounting, financial, legal and political systems, which should ultimately lead to better AQ. This article argues that wealthy economies are likely to report more controlling-useful, but not necessarily more valuation-useful accounting information compared to the poor ones. This argument is based on the fact that on the one hand decision makers within the wealthy economies' capital markets are likely to intensively utilize various alternative sources of information, implying a lower demand on accounting information as a source of valuation decisions. On the other hand, demand for controlling-useful accounting information would exist even while utilizing other (external) sources of information as the inside (managerial) information helps the management to efficiently control and plan the firm activities.

Abstract: Two-stage electricity market clearing is designed to maintain market efficiency under ideal conditions, e.g., perfect forecast and nonstrategic generation. This work demonstrates that the individual strategic behavior of inelastic load participants in a two-stage settlement electricity market can deteriorate efficiency. Our analysis further implies that virtual bidding can play a role in alleviating this loss of efficiency by mitigating the market power of strategic load participants. We use real-world market data from New York ISO to validate our theory.

Abstract: This paper aims to provide a comparative study of the Islamic versus conventional banking sector risk by using market data generated from the sample of publicly listed Islamic and conventional banks in the Gulf Cooperation Council (GCC) region.,The authors introduce a market-based measure of bank stress and test this indicator against the Tier 1 Capital Ratio using Granger causality tests.,The authors find that the market-based measure is a leading indicator of banking stress when compared to the accounting-based Tier 1 ratio and thus is relevant to the Basel regulation’s Pillar 3.,This paper only looks at Islamic vs conventional banks in the Gulf region, and the authors would like to extend this analysis to a broader range of financial institutions, especially in the European and North American markets.,Developing a measure that signals bank stress ahead of typically used measures can help regulators, bank management and investors identify oncoming problems and issues before these become too big to manage.,The results from this analysis provides insight into the offsetting impact from two drivers (beta and book-to-market ratio) of the cost of equity capital for the conventional vs Islamic banking sectors.

Abstract: The analysis of interconnectedness and contagion is an important part of the financial stability and risk assessment of a country’s financial system. This paper offers detailed and practical guidance on how to conduct a comprehensive analysis of interconnectedness and contagion for a country’s financial system under various circumstances. We survey current approaches at the IMF for analyzing interconnectedness within the interbank, cross-sector and cross-border dimensions through an overview and examples of the data and methodologies used in the Financial Sector Assessment Program. Finally, this paper offers practical advice on how to interpret results and discusses potential financial stability policy recommendations that can be drawn from this type of in-depth analysis.

Abstract: We develop a model to predict bankruptcies, exploiting that negative book equity is a strong indicator of financial distress. Accordingly, our key predictor of bankruptcy is the probability that future losses will deplete a firm’s book equity. To calculate this probability, we use earnings forecasts and their standard deviations obtained from cross-sectional regression models in the spirit of Hou, van Dijk, and Zhang (2012). We add variables that we find to discriminate between bankrupt and non-bankrupt firms. As our model requires only accounting data, we can provide bankruptcy predictions for a wide range of firms, including firms that have no access to capital markets. In strictly out-of-sample tests, we show that our accounting model performs better than alternative corporate failure models that use only accounting information. If we additionally allow for stock market information, our approach also outperforms leading alternatives that require market data.