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on Big Data |
| By: | Dengler, Sebastian (Tilburg University, TILEC); Prüfer, Jens (Tilburg University, TILEC) |
| Abstract: | Recent progress in information technologies provides sellers with detailed knowledge about consumers' preferences, approaching perfect price discrimination in the limit. We construct a model where consumers with less strategic sophistication than the seller's pricing algorithm face a trade-off when buying. They choose between a direct, transaction cost-free sales channel and a privacy-protecting, but costly, anonymous channel. We show that the anonymous channel is used even in the absence of an explicit taste for privacy if consumers are not too strategically sophisticated. This provides a micro-foundation for consumers' privacy choices. Some consumers benefit but others suffer from their anonymization. |
| Keywords: | privacy; big data; perfect price discrimination; level-k thinking |
| JEL: | L11 D11 D83 D01 L86 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutil:809f6834-9e85-4449-b21a-67cdb02beacf&r=big |
| By: | Sebnem Kalemli-Ozcan; Luc Laeven; David Moreno |
| Abstract: | We quantify the role of financial factors that have contributed to sluggish investment in Europe in the aftermath of the 2008–2009 crisis. Using a big data approach, we match the firms to their banks based on banking relationships in 8 European countries over time, obtaining over 2 million observations. We document four stylized facts. First, the decline in investment in the aftermath of the crisis can be linked to higher leverage, increased debt service, and having a relationship with a weak bank—once we condition on aggregate demand shocks. Second, the relation between leverage and investment depends on the maturity structure of debt: firms with a higher share of long-term debt have higher investment rates relative to firms with a lower share of long-term debt since the rollover risk for the former is lower and the latter is higher. Third, the negative effect of leverage is more pronounced when firms are linked to weak banks, i.e., banks with high exposure to sovereign risk. Firms with higher shares of short-term debt decrease investment more relative to firms with lower shares of short-term debt even both set of firms linked to weak banks. This result suggests that loan evergreening by weak banks played a limited role in increasing investment. Fourth, the direct negative effect of weak banks on the average firm’s investment disappears once demand shocks are controlled for, although the differential effects with respect to leverage and the maturity of debt remain. |
| JEL: | E22 E32 E44 F34 F36 G32 |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24555&r=big |
| By: | Kevin Kuo |
| Abstract: | We propose a novel approach for loss reserving based on deep neural networks. The approach allows for jointly modeling of paid losses and claims outstanding, and incorporation of heterogenous inputs. We validate the models on loss reserving data across lines of business, and show that they attain or exceed the predictive accuracy of existing stochastic methods. The models require minimal feature engineering and expert input, and can be automated to produce forecasts at a high frequency. |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1804.09253&r=big |
| By: | Guanhao Feng; Jingyu He; Nicholas G. Polson |
| Abstract: | Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed in this way, asset pricing studies can be revisited using multi-layer deep learners, such as rectified linear units (ReLU) or long-short-term-memory (LSTM) for time-series effects. State-of-the-art algorithms including stochastic gradient descent (SGD), TensorFlow and dropout design provide imple- mentation and efficient factor exploration. To illustrate our methodology, we revisit the equity market risk premium dataset of Welch and Goyal (2008). We find the existence of nonlinear factors which explain predictability of returns, in particular at the extremes of the characteristic space. Finally, we conclude with directions for future research. |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1804.09314&r=big |
| By: | Gary Koop (Department of Economics, University of Strathclyde, UK; The Rimini Centre for Economic Analysis); Dimitris Korobilis (Essex Business School, University of Essex, UK; The Rimini Centre for Economic Analysis); Davide Pettenuzzo (Sachar International Center, Brandeis University, USA) |
| Abstract: | Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from the compressed regression literature. It involves randomly compressing the explanatory variables prior to analysis. A huge dimensional problem is thus turned into a much smaller, more computationally tractable one. Bayesian model averaging can be done over various compressions, attaching greater weight to compressions which forecast well. In a macroeconomic application involving up to 129 variables, we find compressed VAR methods to forecast as well or better than either factor methods or large VAR methods involving prior shrinkage. |
| Keywords: | multivariate time series, random projection, forecasting |
| JEL: | C11 C32 C53 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:17-32&r=big |
| By: | Peter Sittler |
| Abstract: | Digitalization is a global phenomenon and has an important impact on real estate industry. A growing importance of digital transformation is expected in the future. Especially the real estate sector with solid, physical and palpable buildings seems to deprive this trend.This research paper will evaluate the recent and future situation of digital real estate trends in particular the DACH region [Germany (D), Austria (A), Switzerland (CH)]. The first part gives an overview to point out the current state of literature and to find a comprehensive analysis of the published literature. Especially the Switzerland publishes outstanding studies in this field. There is a number of publications concerning the future of real estate and the utilized technologies as the meanwhile mainly known buzzwords building information modelling (BIM), internet of things (IoT), augmented (AR) and virtual reality (VR), big data analytics, 3D-printing, cloud solutions and smart concepts. But an overall survey and summary was not yet made. The second part should identify the upcoming topics influencing the real estate industry. The aim is to analyze future business models and trends with their advantages or disadvantages for infrastructure, buildings and companies to structure and classify their future potential. Many new proptechs (consisting of the terms property and technology) coming up to shift the property sector into the digital era, so the development of alternative business models will become essential for real estate business. |
| Keywords: | Business Model; Digitalization; New Technology; proptech; Trends |
| JEL: | R3 |
| Date: | 2017–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2017_128&r=big |