Photo Rachidi Kotchoni

Rachidi Kotchoni

Maître de conférences avec HDR
  • Email
  • Tél. professionnel 0140975947
  • Bureau à Paris Nanterre (Bât. + num.) G517B
  • Axe de recherche

      Macroéconomie Internationale, Banque et Econométrie Financière

  • Thème(s)
    • Econométrie financière
    • Econométrie des séries temporelles
    • Politique de concurrence

2017-33 "Investor Relations' Quality and Mispricing"

Houdou Basse Mama, Rachidi Kotchoni

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Résumé
We investigate the role of corporate investor relations (IR) in the correction process of mispricing. We provide robust evidence of accruals' mispricing for the sub-sample of firms with lower-rated IR. However, mispricing is more pronounced among firms with higher valuation uncertainty. Further analyses show that firms with higher-rated IR on average earn higher returns, and this relation is resilient to known risk/mispricing factors. More important, IR likely has countervailing effects on mispricing. IR may widen the information asymmetry among investors and concomitantly reduce future analyst forecast errors. Overall, high-quality IR appears to facilitate the market's ability to establish efficient stock prices.
Classification-JEL
G12, G14, D82.
Mot(s) clé(s)
Investor relations; Mispricing; Mishkin test; Information asymmetry; In- formation uncertainty.
Fichier

2017-5 "Forecasting economic activity in data-rich environment"

Rachidi Kotchoni, Maxime Leroux, Dalibor Stevanovic

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Résumé
This paper compares the performance of five classes of forecasting models in an extensive out-of-sample exercise. The types of models considered are standard univariate models, factor-augmented regressions, dynamic factor models, other data-rich models and forecast combinations. These models are compared using four types of data: real series, nominal series, the stock market index and exchange rates. Our Findings can be summarized in a few points: (i) data-rich models and forecasts combination approaches are the best for predicting real series; (ii) ARMA(1,1) model predicts inflation change incredibly well and outperform data-rich models; (iii) the simple average of forecasts is the best approach to predict future SP500 returns; (iv) exchange rates can be predicted at short horizons mainly by univariate models but the random walk dominates at medium and long terms; (v) the optimal structure of forecasting equations changes much over time; and (vi) the dispersion of out-of-sample point forecasts is a good predictor of some macroeconomic and financial uncertainty measures as well as of the business cycle movements among real activity series.
Classification-JEL
C55, C32, E17.
Mot(s) clé(s)
Forecasting, Factor Models, Data-rich environment, Model averaging.
Fichier

2016-40 "Forecasting U.S. Recessions and Economic Activity"

Rachidi Kotchoni, Dalibor Stevanovic

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Résumé
This paper proposes a framework to produce real time multi-horizon forecasts of business cycle turning points, average forecasts of economic activity as well as conditional forecasts that depend on whether the horizon of interest belongs to a recession episode or not. Our forecasting models take the form of an autoregression of order one that is augmented with either a probability of recession or an inverse Mills ratio. Our empirical results suggest that a static Probit model that uses only the Term Spread as regressor provides comparable fit to the data as more sophisticated non-static Probit models. We also find that the dynamic patterns of the Term Structure of recession probabilities are quite informative about business cycle turning points. Our most parsimonious augmented autoregressive model delivers better out-of-sample forecasts of GDP growth than the benchmark models considered. We construct several Term Structures of recession probabilities since the last official NBER turning point. The results suggest that there has been no harbinger of a recession for the US economy since 2010Q4 and that there is none to fear at least until 2018Q1. GDP growth is expected to rise steadily between 2016Q3 and 2018Q1 in the range [2.5%,3.5%].
Classification-JEL
C35, C53, E27, E37
Mot(s) clé(s)
Augmented Autoregressive Model, Conditional Forecasts, Economic Activity, Inverse Mills Ratio, Probit, Recession.
Fichier
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