scholarly journals The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 421-435
Author(s):  
Knut Lehre Seip ◽  
Dan Zhang
Keyword(s):  
Yield Curve ◽  
Quadratic Term ◽  
Federal Funds ◽  
Test Set ◽  
Federal Funds Rate ◽  
Out Of Sample ◽  
Sample Test ◽  
Learning Set ◽  
General Movement ◽  
Better Than

Previous studies have shown that the treasury yield curve, T, forecasts upcoming recessions when it obtains a negative value. In this paper, we try to improve the yield curve model while keeping its parsimony. First, we show that adding the federal funds rate, FF, to the model, GDP = f(T,FF), gives seven months vs. five months warning time, and it gives a higher prediction skill for the recessions in the out-of-sample test set. Second, we find that including the quadratic term of the yield curve and the federal funds rate improves the prediction of the 1990 recession, but not the other recessions in the period 1977 to 2019. Third, the T caused a pronounced false peak in GDP for the test set. Restricting the learning set to periods where T and FF were leading the GDP in the learning set did not improve the forecast. In general, recessions are predicted better than the general movement in the economy. A “horse race” between GDP = f(T,FF) and the Michigan consumer sentiment index suggests that the first beats the latter by being a leading index for the observed GDP for more months (50% vs. 6%) during the first test year.

scholarly journals Predicting Phosphorescence Energies and Inferring Wavefunction Localization with Machine Learning

2021 ◽  
Author(s):  
Andrew Sifain ◽  
Levi Lystrom ◽  
Richard A. Messerly ◽  
Justin Smith ◽  
Benjamin Nebgen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Spin Transition ◽  
Strong Relationship ◽  
Triplet States ◽  
Test Set ◽  
Out Of Sample ◽  
Sample Test ◽  
Long Time

<p><a></a><a>Long-time excited state dynamics of triplet states and subsequent emission via phosphorescence are commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained using <i>ab initio</i> datasets may expedite the discovery of phosphorescent compounds. However, we show that standard ML approaches for modeling potential energy surfaces that succeed on small molecules do not generalize to molecules of larger sizes, due to the failure to account for spatial localities in spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the transition energy. Trained on phosphorescent transition energies of organic molecules, the model achieves prediction accuracies of ~4 kcal/mol on the held-out test set and ~13 kcal/mol on an out-of-sample test set of large phosphorescent molecules. These localization weights have a strong relationship with the <i>ab initio</i> spin density of the triplet to singlet state transition, and thus infer localities of the molecule that determine the spin transition, despite that no direct electronic information was provided during training.</a></p>

Asset Valuation

2019 ◽  
pp. 111-120
Author(s):  
Alan N. Rechtschaffen
Keyword(s):  
Interest Rates ◽  
Federal Reserve ◽  
Yield Curve ◽  
Market Demand ◽  
Asset Valuation ◽  
Federal Funds ◽  
Yield Curves ◽  
Federal Funds Rate ◽  
The Great Recession

This chapter begins with a discussion of the use of interest rates in asset valuation. During the Great Recession, the Federal Reserve has navigated U.S. interest rates lower by first reducing the target for the federal funds rate to zero, and then engaging in a process of quantitative easing by purchasing longer-term securities. The effect of the Federal Reserve's actions has been to lower interest rates that affect valuation models across all assets and investments. The chapter then discusses interest rate yield curve, covering the types of yield curves, why the yield curve may be flat or inverted, the increase in market demand for long-term securities, and long-term yield affected by Federal Reserve monetary policy.

scholarly journals The GDP, the US treasury yield and the federal funds rate: who follows whom, when and why?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Knut Lehre Seip ◽  
Dan Zhang
Keyword(s):  
Economic Growth ◽  
Financial Stability ◽  
Yield Curve ◽  
Empirical Work ◽  
Principal Component ◽  
Bond Market ◽  
Economic Activities ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Content Type

Purpose This study aims to address the fundamental question on how the major players in the economy dynamically interact with each other: among the central bank, the investors in the bond market and the firms and consumers that contribute to the economic growth, who gets information from whom, when and why? Design/methodology/approach To answer “who follows whom,” the authors apply a novel technique to examine the lead–lag relations between three time series, the federal funds rate, the treasury yield curve and the gross domestic product (GDP). To investigate “when and why,” the authors combine the lead–lag relations with principal component analysis to cluster economic states that are similar with respect to the eight macroeconomic variables. Findings The authors show that during the period 1977–2019, the bond market potentially obtained information from the federal funds rate (61% of the time) and less often (34% of time) from the changes in the GDP. Meanwhile, the funds rate decision by the Federal Reserve seems to lead the economic growth about 63% of the time. The analysis also suggests that the bond market obtained information directly from GDP when unemployment and inflation was high. In addition, the authors find that the federal funds rate was leading the GDP when the GDP deviated from the target value, consistent with the Federal Reserve’s policy of boosting and damping the economy when the GDP growth is low or high, respectively. Originality/value This study provides insights into the fundamental questions that have important implications for empirical work on the monetary policy, financial stability and economic activities.

Using Connectionist Modules for Decision Support

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak
Keyword(s):  
Decision Support ◽  
Standard Deviation ◽  
Confidence Interval ◽  
Posterior Probability ◽  
Back Propagation ◽  
Connectionist Model ◽  
Test Set ◽  
The Third ◽  
Independent Test ◽  
Better Than

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.

Real-Time Profitability of Published Anomalies: An Out-of-Sample Test

2013 ◽  
Vol 03 (03n04) ◽  
pp. 1350016 ◽  
Author(s):  
Jing-Zhi Huang ◽  
Zhijian Huang
Keyword(s):  
Asset Pricing ◽  
Transaction Costs ◽  
Real Time ◽  
Empirical Evidence ◽  
Equity Market ◽  
Data Snooping ◽  
Asset Pricing Anomalies ◽  
Pricing Anomalies ◽  
Out Of Sample ◽  
Sample Test

Empirical evidence on the out-of-sample performance of asset-pricing anomalies is mixed so far and arguably is often subject to data-snooping bias. This paper proposes a method that can significantly reduce this bias. Specifically, we consider a long-only strategy that involves only published anomalies and non-forward-looking filters and that each year recursively picks the best past-performer among such anomalies over a given training period. We find that this strategy can outperform the equity market even after transaction costs. Overall, our results suggest that published anomalies persist even after controlling for data-snooping bias.

scholarly journals A mixture autoregressive model based on Gaussian and Student’s t-distributions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Savi Virolainen
Keyword(s):  
Interest Rate ◽  
Autoregressive Model ◽  
Autoregressive Models ◽  
Federal Funds ◽  
Treasury Bill ◽  
Federal Funds Rate ◽  
Empirical Application ◽  
Model Based ◽  
Interest Rate Spread ◽  
Student’S T

Abstract We introduce a new mixture autoregressive model which combines Gaussian and Student’s t mixture components. The model has very attractive properties analogous to the Gaussian and Student’s t mixture autoregressive models, but it is more flexible as it enables to model series which consist of both conditionally homoscedastic Gaussian regimes and conditionally heteroscedastic Student’s t regimes. The usefulness of our model is demonstrated in an empirical application to the monthly U.S. interest rate spread between the 3-month Treasury bill rate and the effective federal funds rate.

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