High-low Strategy of Portfolio Composition using Evolino RNN Ensembles

trategy of investment is important tool enabling better investor's decisions in uncertain finance market. Rules of portfolio selection help investors balance accepting some risk for the expectation of higher returns. The aim of the research is to propose strategy of constructing investment portfolios based on the composition of distributions obtained by using high–low data. The ensemble of 176 Evolino recurrent neural networks (RNN) trained in parallel investigated as an artificial intelligence solution, which applied in forecasting of financial markets. Predictions made by this tool twice a day with different historical data give two distributions of expected values, which reflect future dynamic exchange rates. Constructing the portfolio, according to the shape, parameters of distribution and the current value of the exchange rate allows the optimization of trading in daily exchange-rate fluctuations. Comparison of a high-low portfolio with a close-to-close portfolio shows the efficiency of the new forecasting tool and new proposed trading strategy

The Strategy of Professional Forecasting

This paper develops and compares two theories of strategic behavior of profes-sional forecasters. The first theory posits that forecasters compete in a forecasting contest with pre-specified rules. In equilibrium of a winner-take-all contest, forecasts are excessively differentiated. According to the alternative reputational cheap talk theory, forecasters aim at convincing the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts and the realized state. If the market has naive views on forecasters’ behavior, forecasts are biased toward the prior mean. Otherwise, equilibrium forecasts are unbiased but imprecise

Survey Expectations

This paper focuses on survey expectations and discusses their uses for testing and modeling of expectations.Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized. A weak form of the rational expectations hypothesis which focuses on average expectationsrather than individual expectations is advanced. Other models of expectations formation, such as the adaptive expectations hypothesis, are briefly discussed. Testable implications of rational and extrapolative models of expectationsare reviewed and the importance of the loss function for the interpretation of the test results is discussed. The paper thenprovides an account of the various surveys of expectations, reviews alternative methods of quantifying the qualitative surveys, and discusses the use of aggregate and individual survey responses in the analysis of expectations and for forecasting

Bank Regulation and Supervision: What Works Best?

This paper uses our new database on bank regulation and supervision in 107 countries to assess the relationship between specific regulatory and supervisory practices and banking-sector development, efficiency, and fragility. The paper examines: (i) regulatory restrictions on bank activities and the mixing of banking and commerce; (ii) regulations on domestic and foreign bank entry; (iii) regulations on capital adequacy; (iv) deposit insurance system design features; (v) supervisory power, independence, and resources, (vi) loan classification stringency, provisioning standards, and diversification guidelines; (vii) regulations fostering information disclosure and private-sector monitoring of banks; and (viii) government ownership. The results, albeit tentative, raise a cautionary flag regarding government policies that rely excessively on direct government supervision and regulation of bank activities. The findings instead suggest that policies that rely on guidelines that (1) force accurate information disclosure, (2) empower private-sector corporate control of banks, and (3) foster incentives for private agents to exert corporate control work best to promote bank development, performance and stability.

The Tax Sensitivity of Foreign Direct Investment: Evidence from Firm-Level Panel Data

Understanding the determinants of foreign direct investment (FDI) is important for analyzing capital flows and the industrial organization of multinational firms. Most empirical studies of FDI, however, have focused on case studies of nontax factors in overseas investment decisions or on discerning reduced-form relationships between some measure of FDI and variables relating to nontax and tax aspects of the investment decision. In this paper, we examine the effects of taxation on FDI using previously unexplored (for this purpose) panel data on FDI by subsidiaries of U.S. multinational firms collected by Compustat's geographic segment file project. These firm- level data contain information on new capital investment overseas which enable us to measure tax influences on FDI more precisely and allow us to focus on structural models of subsidiaries' investment decision. Our empirical results cast significant doubt on the simplest notion that 'taxes don't matter' for U.S. firms' FDI decisions. Tax parameters influence FDI in precisely the way indicated by neoclassical models. Our results also lend support to the application of the 'tax capitalization' model to the study of dividend repatriation and foreign direct investment decisions.

Financial market prediction system with Evolino neural network and Delphi method

Use of artificial intelligence systems in forecasting financial markets requires a reliable and simple model that would ensure profitable growth. The model presented in the paper combines Evolino recurrent neural networks with orthogonal data inputs and the Delphi expert evaluation method for its investment portfolio decision making process. A statistical study demonstrates the reliability of the model and describes its accuracy. Capabilities of the model are demonstrated using a trading simulation

Distance, Lending Relationships and Competition

A recent string of theoretical papers highlights the importance of geographical distance in explaining pricing and availability of loans to small firms.Lenders located in the vicinity of small firms have significantly lower monitoring and transaction costs, and hence considerable market power if competing financiers are located relatively far.We directly study the effect on loan conditions of the geographical distance between firms, the lending bank, and all other banks in the vicinity.For our study, we employ detailed contract information from more than 15,000 bank loans to small firms and control for relevant relationship, loan contract, bank branch, firm, and regional characteristics.We report the first comprehensive evidence on the occurrence of spatial price discrimination in bank lending.Loan rates decrease in the distance between the firm and the lending bank and increase similarly in the distance between the firm and competing banks.Both effects are statistically significant and economically relevant, are robust to changes in model specifications and variable definitions, and are seemingly not driven by the modest changes over time in lending technology we infer.pricing;bank lending;price discrimination

Distance, Lending Relationships, and Competition

A recent string of theoretical papers highlights the importance of geographical distance in explaining pricing and availability of loans to small firms. Lenders located in the vicinity of small firms have significantly lower monitoring and transaction costs, and hence considerable market power if competing financiers are located relatively far. We directly study the effect on loan conditions of the geographical distance between firms, the lending bank, and all other banks in the vicinity. For our study, we employ detailed contract information from more than 15,000 bank loans to small firms and control for relevant relationship, loan contract, bank branch, firm, and regional characteristics. We report the first comprehensive evidence on the occurrence of spatial price discrimination in bank lending. Loan rates decrease in the distance between the firm and the lending bank and increase similarly in the distance between the firm and competing banks. Both effects are statistically significant and economically relevant, are robust to changes in model specifications and variable definitions, and are seemingly not driven by the modest changes over time in lending technology we infer.spatial price discrimination, bank credit, lending relationships

Composite indicators for monetary analysis

The prominent role assigned to money by the ECB has been the subject of an intense debate because of the declining predictive power of the monetary aggregate M3 for inflation in recent years. This paper reassesses the information content of monetary analysis for future inflation using dynamic factors extracted from a new and richer cross-section of data including the monetary aggregate M3, its components and counterparts, and a detailed breakdown of deposits and loans at sectoral level. Weighting monetary and credit variables according to their signal to noise ratio allows us to downplay those that in recent times contributed significantly to the deterioration of the information content of the M3. Factor-model based inflation forecasts turn out to be more accurate than those produced by traditional competitor models at the relevant policy horizon of six-quarters ahead. All in all, our results support the view that an analysis based on a large set of monetary and credit variables is a more useful tool for assessing risks to price stability than one that simply focuses on the dynamic of the overall monetary aggregate M3.monetary analysis, factor models, forecasting