SSRN Author: Thierry FoucaultThierry Foucault SSRN Content
https://privwww.ssrn.com/author=57561
https://privwww.ssrn.com/rss/en-usThu, 11 Feb 2021 01:26:23 GMTeditor@ssrn.com (Editor)Thu, 11 Feb 2021 01:26:23 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Equilibrium Data Mining and Data AbundanceWe analyze how computing power and data abundance affect speculators' search for predictors. In our model, speculators search for predictors through trials and optimally stop searching when they find a predictor with a signal-to-noise ratio larger than an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, by reducing search costs. In contrast, data abundance can reduce this threshold because (i) it intensifies competition among speculators and (ii) it increases the average number of trials to find a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We derive implications of these effects for the distribution of asset managers' skills and trading profits.
https://privwww.ssrn.com/abstract=3710495
https://privwww.ssrn.com/1990341.htmlWed, 10 Feb 2021 18:41:30 GMTREVISION: Does Alternative Data Improve Financial Forecasting? The Horizon EffectWe analyze the effect of alternative data on the informativeness of financial forecasts. Our starting hypothesis is that the emergence of alternative data reduces the cost of obtaining information about firms' short-term cash-flows more than their long-term cash-flows. If correct, and forecasting short-term and long-term cash-flows are distinct tasks, analysts will reduce effort to process long-term information when alternative data becomes available. Alternative data thus makes long-term forecasts less informative, while increasing the informativeness of short-term forecasts. We confirm this prediction using variations in analysts' exposure to social media data and a new measure of forecast informativeness at various horizons.
https://privwww.ssrn.com/abstract=3702411
https://privwww.ssrn.com/1990338.htmlWed, 10 Feb 2021 18:40:10 GMTREVISION: Does Alternative Data Improve Financial Forecasting? The Horizon EffectWe analyze the effect of alternative data on the informativeness of financial forecasts. Our starting hypothesis is that the emergence of alternative data reduces the cost of obtaining information about firms' short-term cash-flows more than their long-term cash-flows. If correct, and forecasting short-term and long-term cash-flows are distinct tasks, analysts will reduce effort to process long-term information when alternative data becomes available. Alternative data thus makes long-term forecasts less informative, while increasing the informativeness of short-term forecasts. We confirm this prediction using variations in analysts' exposure to social media data and a new measure of forecast informativeness at various horizons.
https://privwww.ssrn.com/abstract=3702411
https://privwww.ssrn.com/1989922.htmlTue, 09 Feb 2021 20:46:55 GMTREVISION: Does Alternative Data Improve Financial Forecasting? The Horizon EffectWe analyze the effect of alternative data on the informativeness of financial forecasts. Our starting hypothesis is that the emergence of alternative data reduces the cost of obtaining information about firms' short-term cash-flows more than their long-term cash-flows. If correct, and forecasting short-term and long-term cash-flows are distinct tasks, analysts will reduce effort to process long-term information when alternative data becomes available. Alternative data thus makes long-term forecasts less informative, while increasing the informativeness of short-term forecasts. We confirm this prediction using variations in analysts' exposure to social media data and a new measure of forecast informativeness at various horizons.
https://privwww.ssrn.com/abstract=3702411
https://privwww.ssrn.com/1988410.htmlFri, 05 Feb 2021 13:16:40 GMTREVISION: Equilibrium Data Mining and Data AbundanceWe analyze how computing power and data abundance affect speculators' search for predictors. In our model, speculators search for predictors through trials and optimally stop searching when they find a predictor with a signal-to-noise ratio larger than an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, by reducing search costs. In contrast, data abundance can reduce this threshold because (i) it intensifies competition among speculators and (ii) it increases the average number of trials to find a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We derive implications of these effects for the distribution of asset managers' skills and trading profits.
https://privwww.ssrn.com/abstract=3710495
https://privwww.ssrn.com/1984982.htmlWed, 27 Jan 2021 14:34:47 GMTREVISION: Does Big Data Improve Financial Forecasting? The Horizon EffectWe study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts forecast short-term and long-term earnings and choose how much information to process about each horizon to minimize forecasting error, net of information processing costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information processing strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. We provide empirical support for this prediction using a large sample of forecasts at various horizons and novel measures of analysts' exposure to abundant data. Data abundance can thus impair the quality of long-term financial forecasts.ty of long-term forecasts.
https://privwww.ssrn.com/abstract=3702411
https://privwww.ssrn.com/1973731.htmlMon, 21 Dec 2020 09:55:10 GMTREVISION: Does Big Data Improve Financial Forecasting? The Horizon EffectWe study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts forecast short-term and long-term earnings and choose how much information to process about each horizon to minimize forecasting error, net of information processing costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information processing strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. We provide empirical support for this prediction using a large sample of forecasts at various horizons and novel measures of analysts' exposure to abundant data. Data abundance can thus impair the quality of long-term financial forecasts.ty of long-term forecasts.
https://privwww.ssrn.com/abstract=3702411
https://privwww.ssrn.com/1967484.htmlWed, 02 Dec 2020 10:18:54 GMTREVISION: Does Big Data Improve Financial Forecasting? The Horizon EffectWe study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts produce forecasts of short-term and long-term earnings and choose how much information to collect about each horizon to minimize their expected forecasting error, net of information acquisition costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information collection strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. Using a large sample of analysts' forecasts at various horizons and novel measures of their exposure to abundant data (e.g., social media data), we provide empirical support for this prediction, which implies that data abundance can impair the quality of long-term forecasts.
https://privwww.ssrn.com/abstract=3702411
https://privwww.ssrn.com/1965182.htmlTue, 24 Nov 2020 16:34:51 GMTREVISION: Does Big Data Improve Financial Forecasting? The Horizon EffectWe study how data abundance affects the informativeness of financial analysts' forecasts at various horizons. Analysts produce forecasts of short-term and long-term earnings and choose how much information to collect about each horizon to minimize their expected forecasting error, net of information acquisition costs. When the cost of obtaining short-term information drops (i.e., more data becomes available), analysts change their information collection strategy in a way that renders their short-term forecasts more informative but that possibly reduces the informativeness of their long-term forecasts. Using a large sample of analysts' forecasts at various horizons and novel measures of their exposure to abundant data (e.g., social media data), we provide empirical support for this prediction, which implies that data abundance can impair the quality of long-term forecasts.
https://privwww.ssrn.com/abstract=3702411
https://privwww.ssrn.com/1962870.htmlTue, 17 Nov 2020 17:03:59 GMTREVISION: Equilibrium Data Mining and Data AbundanceWe analyze how computing power and data abundance affect speculators' search for predictors. In our model, speculators search for predictors through trials and optimally stop searching when they find a predictor with a signal-to-noise ratio larger than an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, by reducing search costs. In contrast, data abundance can reduce this threshold because (i) it intensifies competition among speculators and (ii) it increases the average number of trials to find a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We derive implications of these effects for the distribution of asset managers' skills and trading profits.
https://privwww.ssrn.com/abstract=3710495
https://privwww.ssrn.com/1956111.htmlWed, 28 Oct 2020 08:54:07 GMTREVISION: Inventory Management, Dealers' Connections, and Prices in OTC MarketsWe propose a new model of trading in OTC markets. Dealers accumulate inventories by trading with end-investors and trade among each other to reduce their inventory holding costs. Core dealers use a more efficient trading technology than peripheral dealers, who are heterogeneously connected to core dealers and trade with each other bilaterally. Connectedness affects prices and allocations if and only if the peripheral dealers' aggregate inventory position differs from zero. Price dispersion increases in the size of this position. The model generates new predictions about the effects of dealers' connectedness and dealers' aggregate inventories on prices.
https://privwww.ssrn.com/abstract=3211285
https://privwww.ssrn.com/1956092.htmlWed, 28 Oct 2020 08:38:23 GMTREVISION: Equilibrium Data Mining and Data AbundanceWe analyze how information processing power and data abundance affect speculators' search for predictors. Speculators optimally search for a predictor whose signal-to-noise ratio exceeds an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, because it reduces the cost of search. In contrast, data abundance can lower this threshold because (i) it intensifies competition among speculators, which reduces the benefit of finding a good predictor and (ii) it increases the total expected cost of finding a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We present additional testable implications of these effects.<br>
https://privwww.ssrn.com/abstract=3710495
https://privwww.ssrn.com/1955308.htmlMon, 26 Oct 2020 14:10:29 GMTREVISION: Equilibrium Data Mining and Data AbundanceWe analyze how information processing power and data abundance affect speculators' search for predictors. Speculators optimally search for a predictor whose signal-to-noise ratio exceeds an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, because it reduces the cost of search. In contrast, data abundance can lower this threshold because (i) it intensifies competition among speculators, which reduces the benefit of finding a good predictor and (ii) it increases the total expected cost of finding a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We present additional testable implications of these effects.<br>
https://privwww.ssrn.com/abstract=3710495
https://privwww.ssrn.com/1954530.htmlFri, 23 Oct 2020 08:15:09 GMTREVISION: Equilibrium Data Mining and Data AbundanceWe analyze how information processing power and data abundance affect speculators' search for predictors. Speculators optimally search for a predictor whose signal-to-noise ratio exceeds an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, because it reduces the cost of search. In contrast, data abundance can lower this threshold because (i) it intensifies competition among speculators, which reduces the benefit of finding a good predictor and (ii) it increases the total expected cost of finding a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We present additional testable implications of these effects.<br>
https://privwww.ssrn.com/abstract=3710495
https://privwww.ssrn.com/1951228.htmlWed, 14 Oct 2020 15:34:48 GMTREVISION: Inventory Management, Dealers' Connections, and Prices in OTC MarketsWe propose a new model of trading in OTC markets. Dealers accumulate inventories by trading with end-investors and trade among each other to reduce their inventory holding costs. Core dealers use a more efficient trading technology than peripheral dealers, who are heterogeneously connected to core dealers and trade with each other bilaterally. Connectedness affects prices and allocations if and only if the peripheral dealers' aggregate inventory position differs from zero. Price dispersion increases in the size of this position. The model generates new predictions about the effects of dealers' connectedness and dealers' aggregate inventories on prices.
https://privwww.ssrn.com/abstract=3211285
https://privwww.ssrn.com/1943179.htmlMon, 21 Sep 2020 08:49:42 GMTREVISION: Inventory Management, Dealers' Connections, and Prices in OTC MarketsWe propose a new model of trading in OTC markets. Dealers accumulate inventories by trading with end-investors and trade among each other to reduce their inventory holding costs. Core dealers use a more efficient trading technology than peripheral dealers, who are heterogeneously connected to core dealers and trade with each other bilaterally. Connectedness affects prices and allocations if and only if the peripheral dealers' aggregate inventory position differs from zero. Price dispersion increases in the size of this position. The model generates new predictions about the effects of dealers' connectedness and dealers' aggregate inventories on prices.
https://privwww.ssrn.com/abstract=3211285
https://privwww.ssrn.com/1902905.htmlMon, 01 Jun 2020 08:15:53 GMT