moment matching machine learning methods for risk management of large variable annuity portfolios abstract variable annuity va with embedded guarantees have rapidly grown in popularity around the world in recent years valuation of vas has been studied extensively in past decades however most of the studies focus on a single contract these methods can be extended neither to valuate nor to manage the risk of a large variable annuity portfolio due to the computational complexity in this paper we propose an efficient moment matching machine learning method to solve this problem this method is proved to be a good candidate for risk management in terms of the speed of and the complexity of computing the annual dollar deltas vars and cvars for a large variable annuity portfolio whose contracts are over a period of 25 years there are two stages for our method first we select a small number of contracts and propose a moment matching monte carlo method based on the johnson curve rather than the well known nested simulations to compute the annual dollar deltas vars and cvars for each selected contract then these computed results are used as a training set for well known machine learning methods such as regression tree neural network and so on afterwards the annual dollar deltas vars and cvars for the entire portfolio are predicted through the trained machine learning method compared to other existing methods bauer et al 2008 gan 2013 gan and lin 2015 our method is very efficient and accurate finally our test results support our claims

demand systems with nonstationary prices relative prices are nonstationary and standard root t inference is invalid for demand systems but demand systems are nonlinear functions of relative prices and standard methods for dealing with nonstationarity in linear models cannot be used demand system residuals are also frequently found to be highly persistent further complicating estimation and inference we propose a variant of the translog demand system the ntlog and an associated estimator that can be applied in the presence of nonstationary prices with possibly nonstationary errors the errors in the ntlog can be interpreted as random utility parameters the estimates have classical root t limiting distributions we also propose an explanation for the observed nonstationarity of aggregate demand errors based on aggregation of consumers with heterogeneous preferences in a slowly changing population estimates using u s data are provided

allocative inefficiency and the capital energy controversy abstract the purpose of the paper is to estimate a system of input demands for greek manufacturing deviating from the standard practice of assuming strict cost minimization the study allows for the presence of price distortions and allocative inefficiency in the decision process this assumption affects parameter estimates and estimated elasticities materially and throws new light on the capital energy controversy in greek manufacturing

estimating elasticities of substitution with nested ces production functions where do we stand abstract prompt by an increasing interest by climate change modelers a rich literature estimating elasticities of substitution in a nested ces production framework has recently developed this article reviews such literature we critically describe the nesting structures estimation approaches data sources and aggregation econometric techniques and types of substitution elasticities used by previous literature to offer a comprehensive description of the various options available to a researcher attempting this estimation we also provide suggestions for potential improvements to the estimation process in particular we warn researchers to use ces production functions with caution in empirical applications given their restrictive build in assumptions

estimating theoretically consistent demand systems using cointegration techniques with application to greek food data abstract the use of time series techniques cointegration and error correction models has been offered as a potentially promising way in estimating demand systems that resolve the perennial issue of violating the theoretical postulates of homogeneity and symmetry the empirical results of this paper based on linearized aids and greek food data indicate that sample size aggregation schemes and model specification seem to be important factors in the estimation of a theoretically consistent demand system they also show that homogeneity is sensitive to sample size while symmetry is sensitive to the aggregation scheme adopted

parametric efficiency tests economies of scale and input demand in u s electric power generation duality theory has been applied increasingly in recent years to study the structure of production a major advantage of using duality theory is that systems of input demand equations can be derived by simple differentiation facilitating the use of flexible functional forms also use of duality theory makes it possible to choose a representation of the technology which has desirable properties for estimation such as exogeneity of the regressors however the use of a dual representation of production structure is appropriate only if the corresponding maintained hypothesis concerning economic behavior e g cost minimization or profit maximization is valid the perceived advantages of the dual approach to studying production structure have led to some applications in which the maintained hypothesis concerning economic behavior is unlikely to be appropriate for example neoclassical cost functions have been used to study the characteristics of production in regulated industries despite an extensive theoretical literature indicating that a profit maximizing regulated firm would not minimize costs subject to market prices 2 in this paper we propose a generalized cost function that retains the principal advantages of the neoclassical cost function but does not require that cost minimization subject to market prices be imposed as a maintained hypothesis in our model firms are assumed to base their production decisions on unobservable shadow prices which reflect the effects of regulation on the effective prices of inputs parametric tests for cost minimization are obtained by expressing shadow prices as functions of market prices the generalized cost function is estimated with data for regulated electric utilities the parametric restrictions corresponding to cost minimizing behavior subject to market prices are rejected implying that the use of a neoclassical cost function is not appropriate in this application 3

estimating a non minimum cost function for hospitals hospital costs have been rigorously scrutinized by economists and health policy experts for several decades despite the large volume of research two related problems persist first although theoretical models of hospital behavior such as 9 and 10 suggest systematic deviations from cost minimization there has been little attention given to modelling and measuring this inefficiency second because cost minimization is a questionable maintained hypothesis most hospital cost research has not used cost functions that are dual to production technology consequently the technological concepts of scale and factor substitution are obscured we address these twin problems by developing and estimating a long run cost function that allows for systematic allocative inefficiency the parameter estimates we present also provide information on the traditional production concepts of scale and factor substitution as well as information on the size and source of allocative inefficiency in u s hospitals hospital services clearly comprise a large industry short term hospital expenditures were 120 billion in 1984 for example the u s short term hospital sector has increased in relative size by a factor of 2 5 since 1960 so that short term hospital expenditures are now about 3 6 percent of the gnp in the united states while industry size alone probably justifies an empirical investigation into the production technology for hospital services we are also motivated by the relevance of our findings to several health policy issues measures of scale economies are crucial in forming public policies concerning entry exit and mergers in the hospital industry yet most studies have not actually estimated the technological concept of scale elasticities of factor substitution are also technological and economic concepts that should be of interest to health policy makers these measures indicate the extent to which cost increases can be avoided by substituting away from the factor whose price has increased hospital cost estimates that do not include factor

parametric estimation of technical and allocative inefficiency with panel data the error components approach to estimating allocative inefficiency imposes restrictive assumptions on the distributions of the errors and functional form the parametric approach does not require special assumptions about the error distribution or technology but typically assumes technical efficiency or restrictive functional forms the parametric approach also allows for systematic firm responses to shadow prices the authors generalize the parametric approach to a panel data setting and show that input and firm specific allocative inefficiency as well as firm specific technical inefficiency can be identified and estimated using a flexible functional form this is demonstrated empirically with an application to u s airlines copyright 1994 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

international business education programs in american and non american schools how they are ranked by the academy of international business the academy of international business members were polled to elicit their opinions of the relative quality of graduate international business programs in american schools a second part of the questionnaire asked their opinions about business schools in other countries

effects of allocative inefficiency on resource allocation and energy substitution in pakistan s manufacturing empirical economists often ignore that firms may not minimise costs in the presence of allocative inefficiency assuming strict cost minimisation when actually cost is not minimum may produce biased parameter estimates and elasticities we consider a method for estimating effects of allocative inefficiency on resource allocation and factor substitution and apply it to data from pakistan s manufacturing results show strong evidence of allocative inefficiency leading to over utilization of raw material and capital viz a viz energy and labour and increasing cost of production of firms by 1 per annum computed elasticities diverge from their true values in the presence of allocative inefficiency

technical change and model specification u s agricultural production u s agricultural crop and livestock relationships are examined in the context of both duality and time series econometrics based on time series test results cointegrated models are estimated traditional models generally overestimated the precision of statistical relationships and gave a considerable number of spurious results particularly with regard to technical change when time series properties of the data were addressed there was much less evidence of bias in technical change model specification test results were sensitive both to the time series specification of the maintained model and its functional form preferred functional form depended on the choice criterion

pairs trading performance of a relative value arbitrage rule we test a wall street investment strategy pairs trading with daily data over 1962 2002 stocks are matched into pairs with minimum distance between normalized historical prices a simple trading rule yields average annualized excess returns of up to 11 for self financing portfolios of pairs the profits typically exceed conservative transaction cost estimates bootstrap results suggest that the pairs effect differs from previously documented reversal profits robustness of the excess returns indicates that pairs trading profits from temporary mispricing of close substitutes we link the profitability to the presence of a common factor in the returns different from conventional risk measures wall street has long been interested in quantitative methods of speculation one popular short term speculation strategy is known as pairs trading the strategy has at least a 20 year history on wall street and is among the proprietary statistical arbitrage tools currently used by hedge funds as well as investment banks the concept of pairs trading is disarmingly simple find two stocks whose prices have moved together historically when the spread between them widens short the winner and buy the loser if history repeats itself prices will converge and the arbi

estimation of output and input technical efficiency using a flexible functional form and panel data the farrell 1957 concept of technical efficiency can be measured in two ways denoted output and input technical efficiency the empirical literature does not distinguish between the two measures and confusion exists regarding the required restrictions on technology for the specification of either measure with panel data the authors show how both types of efficiency when treated as fixed effects are to be specified in the context of a translog cost system technical efficiency enters the share equations only in the output case when homotheticity is not imposed the authors estimate their cost systems using panel data on u s airlines copyright 1994 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

separating the signal from the noise financial machine learning for twitter abstract most statistical arbitrage strategies in the academic literature solely rely on price time series by contrast alternative data sources are of growing importance for professional investors we contribute to bridging this gap by assessing the price predictive value of millions of tweets on intraday returns of the s p 500 constituents from 2014 and 2015 for this purpose we design a machine learning system addressing specific challenges inherent to this task at first building on the literature of financial dictionaries we engineer domain specific features along three categories i e directional indicators relevance indicators and meta features next we leverage a random forest to extract the relationship between these features and subsequent stock returns in a low signal to noise setting for performance evaluation we run a rigorous event based backtesting study across all tweets and stocks we find annualized returns of 6 4 percent and a sharpe ratio of 2 2 after transaction costs finally we illuminate the machine learning black box and unveil sources of profitability first results are both driven and limited by the temporal clustering of tweets i e the majority of profits stem from tweets clustered closely together in time corresponding to high event situations second the importance of included features follows an economic rationale e g tweets with positive sentiment tend to yield positive returns and vice versa third we find that stocks of medium market capitalization and from the consumer and technology sectors contribute most to our results which we interpret as a trade off between tweet coverage and tweet relevance

energy savings potential in china s industrial sector from the perspectives of factor price distortion and allocative inefficiency abstract china s industrial energy consumption accounted for 70 82 of national and 14 12 of world energy usage in 2011 in the context of energy scarcity and environmental pollution the industrial sector in china faces unsustainable growth problems by adopting the stochastic frontier analysis sfa framework this paper analyzes the factor allocative efficiency of china s industrial sector and estimates the energy savings potential from the perspective of allocative inefficiency this paper focuses on three issues the first is examining the factor allocative inefficiency of china s industrial sector the second is measuring factor price distortion by the shadow price model the third is estimating the energy savings potential in china s industrial sector during 2001 2009 major conclusions are thus drawn first factor prices of capital labor and energy are distorted in china due to government regulations moreover energy price is relatively low compared to capital price while is relatively high compared to labor price second the industry wide energy savings potential resulted from energy allocative inefficiency was about 9 71 during 2001 2009 the downward trend of energy savings potential implies the increasing energy allocative efficiency in china s industrial sector third a transparent and reasonable pricing mechanism is conducive to improving energy allocative efficiency

deep learning with long short term memory networks for financial market predictions abstract long short term memory lstm networks are a state of the art technique for sequence learning they are less commonly applied to financial time series predictions yet inherently suitable for this domain we deploy lstm networks for predicting out of sample directional movements for the constituent stocks of the s p 500 from 1992 until 2015 with daily returns of 0 46 percent and a sharpe ratio of 5 8 prior to transaction costs we find lstm networks to outperform memory free classification methods i e a random forest raf a deep neural net dnn and a logistic regression classifier log the outperformance relative to the general market is very clear from 1992 to 2009 but as of 2010 excess returns seem to have been arbitraged away with lstm profitability fluctuating around zero after transaction costs we further unveil sources of profitability thereby shedding light into the black box of artificial neural networks specifically we find one common pattern among the stocks selected for trading they exhibit high volatility and a short term reversal return profile leveraging these findings we are able to formalize a rules based short term reversal strategy that yields 0 23 percent prior to transaction costs further regression analysis unveils low exposure of the lstm returns to common sources of systematic risk also compared to the three benchmark models

deep neural networks gradient boosted trees random forests statistical arbitrage on the s p 500 abstract in recent years machine learning research has gained momentum new developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well known and powerful tree based techniques mainly operating on the original feature space all these methods can be applied to various fields including finance this paper implements and analyzes the effectiveness of deep neural networks dnn gradient boosted trees gbt random forests raf and several ensembles of these methods in the context of statistical arbitrage each model is trained on lagged returns of all stocks in the s p 500 after elimination of survivor bias from 1992 to 2015 daily one day ahead trading signals are generated based on the probability forecast of a stock to outperform the general market the highest k probabilities are converted into long and the lowest k probabilities into short positions thus censoring the less certain middle part of the ranking empirical findings are promising a simple equal weighted ensemble ens1 consisting of one deep neural network one gradient boosted tree and one random forest produces out of sample returns exceeding 0 45 percent per day for k 10 prior to transaction costs irrespective of the fact that profits are declining in recent years our findings pose a severe challenge to the semi strong form of market efficiency

factor substitution and energy productivity fluctuation in china a parametric decomposition analysis abstract technical research on energy productivity can support government officials as they evaluate practical energy policies for the future this study proposed a parametric method to decompose china s energy productivity rate of change into six factors based on a theoretical stochastic frontier analysis the method was applied to conduct an empirical study using inter provincial panel data in china from 1995 to 2012 the results highlighted three key points first the general rate of change in energy productivity was mainly influenced by a steady positive rate of change in technical progress combined with a steady negative rate of change in technical efficiency the core factors causing fluctuations in energy productivity included a positive rate of change in the substitution of capital and energy and a negative rate of change in the substitution of labor and energy second from a geographic perspective provinces with a high rate of change in technical progress experienced a weaker deterioration in technical efficiency however the rate of change in technical efficiency tends to decline as the rate of change in technical progress increases third there is a similar changing trend between the substitution of capital and energy and the substitution of labor and energy

membership of editorial boards and rankings of schools with international business orientation using 4 year data 1990 1994 1998 and 2002 we have provided a ranking of schools with international business ib orientation based on the membership of editorial boards of 30 leading international business journals participation on the editorial boards of quality journals is highly selective and should provide a quality indication of the schools both quality unadjusted and adjusted board membership based ranking are calculated in this study several interesting findings are worth noting first us schools play a significant leadership role among the leading ib programs second the findings of this study also show the major contribution of non us schools which confirms the importance of the global nature of the ib discipline third the top ranked schools share a number of characteristics finally the correlation among different ranking criteria can be low particularly for the top ranked schools suggesting that care should be exercised in interpreting school ranking

technical and allocative inefficiencies and factor elasticities of substitution an analysis of energy waste in iran s manufacturing ignoring technical and allocative inefficiencies or embedding one of them alone in a system of input demands may result in biased elasticities we consider a comprehensive model including technical inefficiency in input and output forms and allocative inefficiency and apply it to panel data from iran s manufacturing sector the results show that the presence of both inefficiencies affects the computed elasticities of demand and substitution moreover in spite of current waste of energy in iran s manufacturing the elimination of environmental constraints will prompt the manufacturing firms to increase the utilization of energy relative to both capital and labor

the relative efficiency of public and private firms in a regulated environment the case of u s electric utilities abstract the relative efficiency of privately owned and publicly owned electric utilities is investigated using theoretical and econometric models that allow for the effects of both ownership type and regulation the estimation results indicate that the two types of firms are equally cost inefficient in the united states the average effect of inefficiency is to increase the cost of production by 2 4 percent holding output constant inefficiency increases the average quantities demanded of capital and labor and decreases the average quantity demanded of fuel