Abstract: An extensive benchmark in financial time series forecasting is performed.Best machine learning(ML) methods out-perform best econometric methods.The ML methodology employed significantly affects forecasting accuracy.Market maturity, forecast horizon & model-assessment method affect forecast accuracy.Evidence against the informational value of technical indicators. Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.

Abstract: This study documents the determinants of financial inclusion in Africa for the period 2005 to 2014, using the dynamic panel data approach. The study finds that per capita income, broad money (% of GDP), literacy, internet access and Islamic banking presence and activity are significant factors explaining the level of financial inclusion in Africa. Domestic credit provided by financial sector (% of GDP), deposit interest rates, inflation and population have insignificant impacts on financial inclusion. The findings of this study are of utmost value to African central banks, policymakers and commercial bankers as they advance innovative approaches to enhance the involvement of excluded poor people in formal finance in Africa.

Abstract: Recently, exchanges have been directly selling market data. We analyze how this practice affects price discovery, the cost of capital, return volatility, market liquidity, information production, and trader welfare. We show that selling price data increases the cost of capital and volatility, worsens market efficiency and liquidity, and discourages the production of fundamental information relative to a world in which all traders observe prices. Generally, allowing exchanges to sell price information benefits exchanges and harms liquidity traders. Overall, our results suggest that regulations on selling market data can play an important role in improving market quality and trader welfare.

Abstract: Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. This talk proposes the use of model predictive control to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using model predictive control when estimates of future returns are updated repeatedly, since the optimal control actions are reconsidered anyway every time a new observation becomes available. Results from testing the approach on market data are presented and compared with previous, rule-based approaches. Further, imposing a trading penalty that reduces the number of trades is discussed as a way to increase the robustness of the approach. (Less)

Abstract: Expanding access to financial services holds the promise to help reduce poverty and foster economic development. However, little is still known about the determinants of the outreach of financial systems across countries. Our study is the first attempt to employ a large panel of countries, several indicators of financial inclusion and a comprehensive set of bank competition measures to study the role of banking system structure as a determinant of cross-country variability in financial outreach for households. We use panel data from 83 countries over a 10-year period to estimate models with both country and time fixed effects. We find that greater banking industry concentration is associated with more access to deposit accounts and loans, provided that the market power of banks is limited. We find evidence that countries in which regulations allow banks to engage in a broader scope of activities are also characterized by greater financial inclusion. Our results are robust to changes in sample, data, and estimation strategy and suggest that the degree of competition is an important aspect of inclusive financial sectors.

Abstract: This paper examines herd behaviour using aggregate market data for stocks, with a focus on the role of idiosyncratic participants with heterogeneous information. We look at herding asymmetry between up and down markets, taking into consideration the daily price limits and the impact of the recent financial crisis. We also improve upon existing tests for fundamental and non-fundamental herding, as well as proposing a method for investigating herd behaviour of different groups of investors. Empirical evidence based on the Ho Chi Minh Stock Exchange in Vietnam reveals a greater level of herding on up compared to down market days, and a significant reduction in the magnitude of herding following the crisis. We document robust intentional herding even when unintentional (fundamental) herding is factored out. Our empirical results also uncover potential within-group herding and between-group interactions among arbitrageurs and noise traders in the market.

Abstract: Our aim is to investigate the sensitivity of financial sector stock returns to market, interest rate, and exchange rate risk in three financial sectors (financial services, banking, and insurance) in eight countries, including various European, the US, and China economies, over the period 2006–2009 during the financial crisis. The econometric framework is a four-variate GARCH-in-mean model and volatility spillovers. The empirical results show the significant effects (positive and negative, respectively) of the stock market returns, interest rate, and exchange rate volatility of the financial sector during the crisis. Besides, we find, in most cases, significant (positive and negative, respectively) volatility spillovers from market return, interest rate, exchange rate, and interest rate in the financial services and the banking sector both in the European and the US economies during the financial crisis.

Abstract: The paper provides a stochastic framework for financial network modelsIt is based on a conditional graphical Gaussian modelSystemic risk is decomposed into country risk plus bank specific riskIt is the first paper that considers more data sources in systemic risks estimation Financial network models are a useful tool to model interconnectedness and systemic risks in banking and finance Recently, graphical Gaussian models have been shown to improve the estimation of network models and, consequently, the interpretation of systemic risksThis paper provides a novel graphical Gaussian model to estimate systemic risks The model is characterised by two main innovations, with respect to the recent literature: it estimates risks considering jointly market data and balance sheet data, in an integrated perspective; it decomposes the conditional dependencies between financial institutions into correlations between countries and correlations between institutions, within countriesThe model has been applied to study systemic risks among the largest European banks, with the aim of identifying central institutions, more subject to contagion or, conversely, whose failure could result in further distress or breakdowns in the whole system The results show that, in the transmission of systemic risk, there is a strong country effect, that reflects the weakness or the strength of the underlying economies Besides the country effect, the most central banks are those larger in size

Abstract: Computer-based algorithms & models have become important in trading in financial markets. We illustrate the significance of model analysis of financial systems by a case study of BlackRock’s analytical platform called ‘Aladdin’. The nature of the model used in a computer algorithm is central to its real performance. Unreal models in financial algorithms will yield inaccurate performances. We review five fundamental models of economic dynamics: (1) traditional price-equilibrium of a commodity market, (2) Keynes-Minsky financial transactions over time, (3) price-disequilibrium of a financial market, (4) investment bank market disequilibrium process, and (5) disequilibrium financial grid of international capital flows. Empirically-valid graphic models are necessary – in order to methodologically develop societal-useful normative economic theory -- based upon the real natural-experiments of societies in economic history.

Abstract: A trading interface is provided for displaying market data related to a tradeable object being traded at an electronic exchange. According to one example embodiment, market data related to a tradeable object is displayed in relation to a value axis, such as a price axis. As new market data is received, the displayed market data is updated and may be repositioned so that a trader can view current market conditions in a viewable portion of the interface. The interface also includes a number of market movement indicators that assist a trader in tracking market movement. These viewable references allow a trader to navigate and immediately understand the “real” direction of the market activity despite any underlying adjustment of the viewable area of the trading interface.

Abstract: The design of environmental trading systems induces specific features of the emission permit price dynamics. In this paper, we evaluate the performance of reduced-form models for emission markets that capture these features in a simplified way and are still feasible for calibration to permit spot, futures, and option prices. Using market data from the European Union Emissions Trading System as the world's largest environmental market, we show that appropriately specified reduced-form models outperform standard approaches with respect to both the historical fit to futures prices and the option pricing performance. Moreover, the performance of reduced-form models critically depends on their consistency with the design of the emission trading system.

Abstract: This article explores the expansion of the Russian state into financial markets after the 2008 global financial crisis. The main argument is that the Russian state has been unable to pursue its own developmental agenda in the sector despite increased regulation and state takeovers. While independent private market participants were pushed aside by state-controlled financial intermediaries, the state failed to follow its own policy strategy towards establishing an international financial centre in Moscow. Instead, the Russian financial market institutions were rendered into a vehicle for inter-bank lending under control of the Central Bank of Russia. Data from Russian stock market and corporate bond market trading highlights the trend. The study discusses the role played by informal power networks in redistribution of state-controlled resources and financial flows, and how this factor influenced the state regulation of financial markets in Russia.

Abstract: Information technology (IT) service providers competing for high valued contracts need to produce a compelling proposal with competitive price. The traditional approach to pricing IT service deals, which builds up the bottom-up costs from the hierarchy of services, is often time consuming, resource intensive, and only available late as it requires granular information of a solution. Recent work on top-down pricing approach enables efficient and early estimates of cost and prices using high level services to overcome and complement these problems. In this paper, we describe an extended pricing method for top-down pricing using the secondary service level. The method makes use of data lower level services to calculate improved estimates, yet still requires minimal input. We compare the previous and new approaches based on industrial data on historical and market deals, and demonstrate that the new approach can generate more accurate estimates. In addition, we also show that mining historical data would yield more accurate estimation than using market data for services, experimental results are in consistent with our findings in previous work.

Abstract: INTRODUCTION Propelled by the growth in commercial usage of the internet by companies and customers, Big Data has emerged as a scientific and marketing discipline which gathers, analyzes, and extracts informational value from massive amounts of business and customer online interactions. The growth of publicly available unstructured data is immense and pervasive --and it is growing at an accelerated pace according to Jonathan Shaw (2014). Shaw makes the case for Big Data: Data now stream from daily life: from phones and credit cards and televisions and computers; from the infrastructure of cities; from sensor-equipped buildings, trains, buses, planes, bridges, and factories. The data flow so fast that the total accumulation of the past two years--a zettabyte--dwarfs the prior record of human civilization (Shaw, 2014, p. 30). Many companies and institutions have quickly recognized the value of harnessing Big Data. Allouche (2014) suggests that applying the science and discipline of Big Data to the massive amounts of unstructured marketing data can leverage that data to innovate their advertising programs. He also suggests that it is important to help companies advertise to customers for the things they want and use--without being annoying. Forbes Magazine (Whitler, 2015) supports the virtues of Big Data and the analysis of that data using Advertising Analytics. Advertising Analytics, a term popularized in the academic literature by Nichols (2013), refers to the set of capabilities that allows marketing firms to make sense of Big Data, ultimately enabling the measurement of an advertising campaign's impact on their business. Jobs, Aukers, and Gilfoil (2015) studied this emerging Big Data/Advertising Analytics discipline and developed a consolidated framework and typology of the firms operating in the ecosystem. It is evident from this work that, even though the ecosystem is thriving and rapidly evolving, the discipline is complex and sometimes difficult to understand. While there are some clear lines of distinction in the types of Big Data and Advertising Analytic firms in the ecosystem, there are some blurry lines, overlap, and interdependence between the constituent firms. Furthermore, some marketing clients are still not convinced about the overall value of Big Data (Ross, Beath, & Quaadgras, 2013), while others are questioning the affordability of Big Data and Advertising Analytic firms, the steepness of their learning curves, how to use them to drive their marketing strategies, or how they can be used to improve marketing efficiency and effectiveness (Purohit, 2014; IBM, 2013; Duke CMO Survey, 2013; Moorman, 2013). Purohit (2014), in particular, suggests that there is a strong enough incentive to overcome the barriers to using Big Data, provided that companies understand the power of this data and analytics to deliver higher throughput, better value for customers, and the immaculate growth in the global economy. While the potential power of Big Data and Marketing Analytics can readily be detailed, a key challenge lies is integrating Big Data into a client company's overall strategy. It requires a significant commitment of resources in terms of money, staff, and time--and the organization needs a plan on how to execute. A recent McKinsey report (Biesdorf, Court, & Willmott, 2013) underscores this point while noting that CIO's must also stress the need to completely remake company data architectures and applications. The report concludes that the missing step for most companies is spending the time to understand how data, analytics, frontline tools, and people can come together to create business value. The problem being addressed in this research endeavor is that the Big Data and Advertising Analytics ecosystem is complex and still evolving. While there is much hype about the industry and its promises to enhance marketing (advertising) program efficiencies and effectiveness, many potential marketing clients (i. …

Abstract: A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.

Abstract: This paper provides evidence on the impact of financial derivatives on the performance of firms in the financial sector in Ghana. Secondary data on financial derivatives, controlled business risks and business performance in terms of return on investment are used for the period 2008-2012. Data are sourced from 23 randomly selected financial firms in Accra, Ghana. A quantitative research technique is used to test four hypotheses. A strong positive correlation between financial derivatives and controlled business risks is found, r (92) = .703, p

Abstract: A new options-pricing formula applies to far-out-of-the money put options on the overall stock market when disaster risk is the dominant force, the size distribution of disasters follows a power law, and the economy has a representative agent with Epstein-Zin utility. In the applicable region, the elasticity of the put-options price with respect to maturity is close to one. The elasticity with respect to exercise price is greater than one, roughly constant, and depends on the difference between the power-law tail parameter and the coefficient of relative risk aversion, γ. The options-pricing formula conforms with data from 1983 to 2015 on far-out-of-the-money put options on the U.S. S&P 500 and analogous indices for other countries. The analysis uses two types of data—indicative prices on OTC contracts offered by a large financial firm and market data provided by OptionMetrics, Bloomberg, and Berkeley Options Data Base. The options-pricing formula involves a multiplicative term that is proportional to the disaster probability, p. If γ and the size distribution of disasters are fixed, time variations in p can be inferred from time fixed effects. The estimated disaster probability peaks particularly during the recent financial crisis of 2008-09 and the stock-market crash of October 1987.

Abstract: Using data from 15 European Union economies, we quantify the real effects of supply-side frictions due to the financial disintegration of European countries since the 2008 financial crisis. We develop a multi-country general equilibrium model with heterogeneous countries and destination-specific financial frictions. Financial institutions allocate capital endogenously across countries, determining the cost of capital to firms and the wealth of nations. The cost of financial disintegration is reduced access to capital for firms which results in lower output. Financial disintegration leads to a 0.54% fall in output in Europe since the crisis. We also estimate benefits of further financial integration.

Abstract: In recent years so-called stochastic power producers (with portfolios including wind and solar power generation capacities) are increasingly asked to participate in electricity markets under the same rules than for conventional generators. Stochastic power producers may act strategically in order to decrease expected penalties induced by imbalances. Many alternative offering strategies based on forecasts in various forms are available in the literature. However, they assume some form of knowledge of future market state and potential balancing prices. In contrast here, we explore whether algorithms could readily learn from market data and deduce how to offer strategically in order to maximize expected market revenues. Our analysis shows that a direct reinforcement learning algorithm can track the nominal level of the optimal quantile forecast to trade in the day-ahead market, while yielding higher revenues than existing benchmark strategies.

Abstract: The ability of ultra-low latency to process market data feed is the premise and foundation for a today's trading system to grab the instant trading profits. The market data feed containing up-to-date information on market changes is multicasted real-timely from financial exchanges to market participants, usually in the form of financial information exchange (FIX) Adapted for STreaming (FAST) protocol. FAST is a differential compression protocol which significantly reduces the bandwidth requirement to transmit market data. However, it also increases the complexity and latency of market data processing. This paper describes a customized architecture for ultra-low latency of market-data processing. Firstly, we propose a bus-based architecture of market-data decoding on Field Programmable Gate Array (FPGA). Our design is a loose-coupled and scalable architecture which is easy to adapt to different FAST templates by connecting different decoders to the main bus. Then we further exploit a dedicated pipelined design to improve the architecture. The pipelined architecture decompresses multiple messages in parallel, overcoming the challenge of data dependency between consecutive differential encoded (FAST) messages. Finally, we implement two prototypes in RTL code and evaluate them on a Xilinx Kintex-7 FPGA. Real test results show that 1) the pipelined processor gains 180% speedup compared with the non-pipelined processor; 2) it achieves an ultra-low decoding latency of 307 ns per message, which is 2 orders of magnitude faster than the software solution.

Abstract: This study examines whether and how an increase in market transparency affects firm disclosure. Market transparency determines how much market data are publicly available for investors to use, while firm disclosure affects the extent to which investors can gain access to firms’ inside information. By using a unique setting in which the frequency of publicly available short-interest data increased exogenously, this paper shows that increasing short-selling transparency has a positive effect on firms’ voluntary disclosure around the short-interest announcement date. This setting also enables me to examine disclosure behavior before a prescheduled arrival of a public signal as featured in the dynamic disclosure theory of Acharya, DeMarzo, and Kremer (2011). The empirical evidence sheds light on managers’ desire to preempt bad news before it might be revealed by other sources, and informs regulators regarding the underexplored effect of market transparency on firms’ disclosure behavior.