A brief biography of Dr Andy Yeh

A brief biography of Dr Andy Yeh (PhD, MFE, MMS, BMS, FRM, and USPTO patent accreditation)

Dr Andy Yeh is responsible for ensuring maximum sustainable member growth within the Andy Yeh Alpha (AYA) fintech network platform ecosystem. Andy supports the core mission of promoting greater financial literacy, inclusion, and freedom of the global general public. Andy integrates multiple team efforts and endeavors to help enrich the economic lives of others. AYA fintech network platform provides fresh economic insights into stock market news, investment memes, business practices, personal finance tools, as well as generic life inspirations.

Andy implements the lean startup approach to incubating our AYA fintech network platform through iterative continuous improvements and feature enhancements. Andy serves as a financial economist, founder, as well as inventor of an algorithmic system for dynamic conditional asset return analysis and fintech network platform automation (U.S. patent publication #15480765 October 2018; Google Patents: https://patents.google.com/patent/US11599946B2).

Andy has rich extensive international experiences in monetary, fiscal, and financial stability policies. His brainchildren or research publications appear in a reasonable range of both academic and professional journals in macro finance, asset return prediction, algorithmic factor quantification, cash capital structure, financial risk management, personal finance, and corporate ownership and governance.

Andy maintains a rich library of literature reviews, study notes, and mathematical exercises in investment theory and evidence, econometric theory, macroeconomic theory and evidence, empirical corporate finance, and both English and Chinese bible verses for Christian faith.

Prior to orchestrating our AYA fintech network platform as a new online social community for stock market investors, Andy has served as a financial economist at several international organizations in America, New Zealand, and Taiwan. These organizations include Academia Sinica, Bank of America, Federal Reserve Bank of San Francisco, Reserve Bank of New Zealand, Institute for Information Industry, Moody's Analytics, and so forth.

Andy was born and bred in Taipei; spent 12 years from high school to his first full-time job in New Zealand; studied and worked another 5 years from graduate school to a Vice President appointment in San Francisco, California, USA; and spent concurrent PhD and lean startup years for more than 10 years in Taipei, Taiwan.

His lean enterprise Brass Ring International Density Enterprise (BRIDE) received first corporate incorporation and registration in Hong Kong.  

Andy holds several academic degrees and qualifications with scholarship support such as Doctor of Philosophy (PhD) in macro finance from National Taiwan University, Master of Financial Engineering (MFE) from the University of California at Berkeley, Master of Management Studies (MMS) and Bachelor of Management Studies (BMS) both with first-class honors from the University of Waikato, and Financial Risk Manager (FRM) global risk industry accreditation.

An up-to-date soft copy of Dr Andy Yeh's curriculum vitae is available upon request.

 

USPTO fintech patent approval, accreditation, and protection for 20 years

With U.S. fintech patent approval, accreditation, and protection for 20 years, our AYA fintech network platform provides proprietary alpha stock signals and personal finance tools for stock market investors worldwide.

We build, design, and delve into our new and non-obvious proprietary algorithmic system for smart asset return prediction and fintech network platform automation. Unlike our fintech rivals and competitors who chose to keep their proprietary algorithms in a black box, we open the black box by providing the free and complete disclosure of our U.S. fintech patent publication. In this rare unique fashion, we help stock market investors ferret out informative alpha stock signals in order to enrich their own stock market investment portfolios. With no need to crunch data over an extensive period of time, our freemium members pick and choose their own alpha stock signals for profitable investment opportunities in the U.S. stock market.

Smart investors can consult our proprietary alpha stock signals to ferret out rare opportunities for transient stock market undervaluation. Our analytic reports help many stock market investors better understand global macro trends in trade, finance, technology, and so forth. Most investors can combine our proprietary alpha stock signals with broader and deeper macro financial knowledge to win in the stock market.

Through our proprietary alpha stock signals and personal finance tools, we can help stock market investors achieve their near-term and longer-term financial goals. High-quality stock market investment decisions can help investors attain the near-term goals of buying a smartphone, a car, a house, good health care, and many more. Also, these high-quality stock market investment decisions can further help investors attain the longer-term goals of saving for travel, passive income, retirement, self-employment, and college education for children. Our AYA fintech network platform empowers stock market investors through better social integration, education, and technology.

 

As of early-January 2023, the U.S. Patent and Trademark Office (USPTO) has approved our U.S. utility patent application: Algorithmic system for dynamic conditional asset return prediction and fintech network platform automation (Google Patents: https://patents.google.com/patent/US11599946B2).

On 4 March 2021, we filed a U.S. patent continuation application (Application Number: #17192059; Publication Number: US20210192628) with a new set of claims in accordance with the April 2017 initial application (Application Number: #15480765; Publication Number: US20180293656).

We went through many USPTO office actions, rejections, failures, setbacks, and other technical obstacles. Eventually, our patent efforts came to fruition in time. We paid the USPTO maintenance fees to secure our patent protection and accreditation for 20 years.

 

Dr Andy Yeh (online brief biography)

Co-Chair (PhD, MFE, MMS, BMS, and FRM)

AYA fintech network platform

Brass Ring International Density Enterprise ©

 

Doctor of Philosophy (PhD) in Macro Finance with Ministry of Science and Technology (MoST) PhD Scholarship and NTU Presidential Award, National Taiwan University (NTU), Taipei, Taiwan.

Master of Financial Engineering (MFE) with William Georgetti Scholarship, University of California at Berkeley (UCB), California, USA.

Master of Management Studies (MMS) with First Class Honors in Econ and Finance, University of Waikato (UW), Hamilton, New Zealand.

Bachelor of Management Studies (BMS) with First Class Honors in Econ and Finance, University of Waikato (UW), Hamilton, New Zealand.

Financial Risk Manager (FRM) global risk industry accreditation, Global Association of Risk Professionals (GARP), New Jersey, USA.

U.S. Patent and Trademark Office (USPTO) fintech patent approval, accreditation, and protection for 20 years, Virginia, USA. 

 

 

Dr Andy Yeh’s research interests span macro-finance, asset return prediction, financial technology, financial regulation, and corporate governance. Below we describe each of his recent research publications. For each of these recent research publications, we provide a hyperlink to the free, open-access, and downloadable preprint PDF file available on the Social Science Research Network (SSRN). For both completeness and accuracy, each preprint PDF file comprises the full original research paper plus the online appendix with more technical details.

 

Why does the neural network with random Fourier features perform so well in asset return prediction?

We answer the question by applying Fourier series expansion to the average asset return function. In the single-factor case, the market return explains only sine components of the neural network with random Fourier features (thereafter the model). The piecemeal addition of one factor at a time cannot capture cosine components of the model. In the multi-factor case with covariance between the market return and hedge-asset return, the Fourier series expansion captures both sine and cosine components of the model. Alpha encodes the longer-run historical path-dependence of this covariance risk. We adapt this analysis to further illuminate the equity premium puzzle.

Keywords: macrofinance, asset return prediction, alpha, Fourier series expansion, cross covariance, equity premium puzzle, neural networks, random Fourier features, and machine-learning algorithms.

 

Return Resonance

By deriving the Fourier transform of asset return covariance, we can convert asset return data in the time domain into both the spectral density and recurrent resonance of asset return data in the frequency domain. As part of this analysis, we consider both the Gaussian exponential square kernel and the more general Matern kernel for asset return covariance. Specifically, we can draw a distinction between the long-term low-frequency cycles and the sudden high-frequency components of asset returns. This equity premium analysis helps us better appreciate the recent virtue of complexity with random Fourier features in macro-finance and asset return prediction.

Keywords: macro-finance; asset return prediction; covariance; equity premium puzzle; Matern and Gaussian exponential square covariance kernels; neural networks; random Fourier features; and machine-learning algorithms.

 

Asset Bubbles versus Steady States

By deriving the Laplace transform of asset return covariance, we can convert asset return data into the spectral return resonance in the frequency domain. This analysis takes into account both the Gaussian exponential-square kernel and the more general Matern kernel for asset return covariance. Specifically, this analysis allows us to better appreciate the broader market as an asset bubble or a dynamically stable system. To the extent that this analysis shines new light on return covariance concentration, this concentration reveals the hidden, highly complex, and non-linear asset return covariances, interactions, and other dynamic anomalies in macro-finance and asset return prediction.

Keywords: macro-finance; asset return prediction; covariance; equity premium puzzle; Matern and Gaussian exponential square covariance kernels; neural networks; random Fourier features; and machine learning algorithms.

 

Algorithmic system for dynamic conditional asset return prediction and fintech network platform automation

The current U.S. fintech patent invention pertains to the novel, nonobvious, and applicable design and development of an algorithmic system for dynamic conditional asset pricing output and financial intelligence technology platform automation. Core technicality entails the consistent estimation of dynamic conditional alphas after one controls for myriad fundamental characteristics such as market risk, size, value, momentum, asset investment growth, and operating profitability through recursive multivariate filtration. Conditional specification test evidence supports the use of the dynamic conditional multifactor asset pricing model against the static alternatives. The fintech platform allows users to interact with one another by transmitting valuable units of financial intelligence and information in an online social network. The information units include dynamic conditional alpha rank order, key financial ratio summary, quadripartite visualization of financial data, and financial statement analysis. The fintech platform automates social network functions for better interactive engagement through minimum viable cloud computing facilities for mobile app design.

Keywords: financial technology; macro-finance; asset return prediction; algorithmic system; dynamic conditional alpha; fintech network platform automation; and social media for stock market investors.

 

Stock market alphas help predict macroeconomic innovations and vice versa.

We extract dynamic conditional factor premiums from the Fama-French factor model and find that most anomalies disappear after one accounts for time variation in these premiums. Vector autoregression evidence shows that mutual causation between dynamic conditional alphas and macroeconomic surprises serves as a core qualifying condition for fundamental factor selection. This economic insight is an incremental step toward drawing a distinction between rational risk and behavioral mispricing models. To the extent that dynamic conditional alphas can reveal the marginal investor’s fundamental news and expectations about the cross-section of average asset returns, our economic insight helps enrich macroeconomic asset return prediction.

Keywords: Fama-French factor models; ARMA-GARCH market risk volatility models; vector auto-regressions; Granger causation tests; dynamic conditional alphas; macroeconomic innovations; and asset return anomalies.

 

Bank leverage and capital bias adjustment through the macroeconomic cycle

We assess the quantitative effects of the recent proposal for more robust bank capital adequacy (Admati and Hellwig, 2013; Myerson, 2014). Our theoretical proof and evidence accord with the core thesis that banks become more stable by increasing its equity capital cushion to absorb extreme losses in times of severe financial stress. This analysis contributes to the ongoing policy debate on total capital adequacy. Our Monte Carlo simulation helps develop an analytical solution for the default probability adjustment through the macroeconomic cycle. This study poses a conceptual challenge to the normative view that banks should maintain high leverage over time.

Keywords: bank leverage; capital; macroeconomic default probability adjustment; Monte Carlo simulation; macro-prudential stress test; loan portfolio segmentation; and logit default likelihood analysis.

 

The Happy Story of Small Business Finance

We empirically examine the American and British survey datasets for about 16,000 privately held small businesses. The financial behavior of private firms demonstrates substantial financial contentment. We find fewer than 10% of the British firms seek rapid firm expansion while only 1.32% of American private firms view a lack of capital other than working capital as a major financial problem. Financial performance indicators such as sales growth, return-on-assets, and net profit margin are insignificant determinants of small business finance. This evidence contradicts the conventional financial lifecycle paradigm of Berger and Udell (1995, 1998, 2002). Younger and less educated private-firm owners more actively use external finance even though more education reduces the fear of bank loan denial, whereas, older and wiser small business owners with better education are less likely to tap into external finance.

Our financial contentment hypothesis for privately held firms also extends to small businesses that seek rapid firm expansion. These high-growth firms participate more in the bank loan markets than low-growth firms. In stark contrast to the financing-gap hypothesis of Berger and Udell (1995, 1997, 2002), our financial contentment hypothesis observes the importance of both social networks and connections for small business finance and in turn confirms the empirical nexus between private owner involvement and sustainable growth. In this light, small private firms serve as a robust investment vehicle for long-term sustainable development.

Overall, our empirical evidence sheds skeptical light on the theoretical plausibility of the agency lifecycle prediction that the vast majority of private firms suffer from severe financial constraints or financing gaps (Jensen and Meckling, 1976; Jensen, 1986; Stulz, 1990; Lang, Stulz, and Walking, 1991; Berger and Udell, 1995, 1998, 2002; Ang, Cole, and Lin, 2000; Bitler, Moskowitz, and Vissing-Jorgensen, 2005). The preponderance of our empirical results proposes a case for an alternative theory of corporate finance for privately held firms that differ from publicly traded corporations in many fundamental ways. This proposal calls for a paradigm shift in rethinking about the conventional wisdom that private firms cannot grow as fast as their public counterparts due to a lack of reasonable access to external capital.

Keywords: corporate finance; small-to-medium enterprise (SME); capital structure; financial contentment; and long-term sustainability.

 

Permanent Capital Hypothesis

We design a dynamic stochastic general equilibrium (DSGE) structural model with Epstein-Zin recursive preferences to gauge the marginal investor’s stochastic discount factor with financial intermediary capital. This model embeds and enriches the cash-flow and discount-rate channels of both the q-theory and intertemporal CAPM. GMM dynamic panel analysis shows that there is a significant trade-off between cash payout and intertemporal debt substitution in the form of foregone tax shields with higher equity capital ratios. We design a new fundamental factor with better-minus-worse (BMW) capital strength to modify the Fama-French (2018) 6-factor model. The resultant 7-factor model helps explain some recent anomalies.

Keywords: permanent capital hypothesis; DSGE; GMM; Fama-French; Epstein-Zin; intermediary capital; q-theory; intertemporal CAPM; cash payout; and debt substitution.

 

Is international divergence from wide stock ownership dispersion an optimal corporate outcome?

We design a model of corporate ownership and control to assess Berle-Means convergence toward diffuse incumbent stock ownership. Berle-Means convergence occurs when legal institutions for investor protection outweigh in relative importance the firm-specific protection of shareholder rights. While these arrangements are complementary sources of investor protection, Berle-Means convergence draws the corporate outcome to the socially optimal quality of corporate governance. High ownership concentration creates perverse incentives for inside blockholders to steer major business decisions to the detriment of both minority shareholders and outside blockholders. Our analysis sheds skeptical light on high insider stock ownership with managerial entrenchment and rent protection.

Keywords: dynamic convergence of Berle-Means incumbent stock ownership dispersion; law and finance; corporate governance; investor protection; managerial entrenchment; and rent protection.

 

Credit default swap spreads, fair value spreads, and interest rate dynamics

This paper examines the empirical relationship between credit risk and interest rate risk. We use the credit default swap (CDS) spread as our measure of credit risk. Also, we control for the variation in the fair-value spread that combines multiple sources of default risk, including the market price of risk (Sharpe ratio), the loss given default (LGD), and the expected default frequency (EDF). After taking into account the fair-value spread, a liquidity risk factor, and several proxies for the general state of the macroeconomy, we find that the interest rate surprise factor serves as a robust determinant of CDS spread gyrations in both the full sample and most subsamples organized by industry type and credit rating status. Furthermore, we empirically find that the swap interest rate variables convey material information about CDS spread movements above and beyond the Treasury interest rate variables in the vast majority of 2SLS regressions. These empirical results have important implications for the parameterization of interest rate dynamics in the Monte Carlo simulation of economic capital for a typical bank's credit portfolio.

Keywords: credit risk; interest rate risk; credit default swap; fair value spread; and panel data regression analysis.

 

An empirical implementation of CreditGrades

We use the CreditGrades credit risk model to value credit default swap (CDS) spreads for public companies at the intersection of the S&P 100 index and Moody's Bottom Rung report for the global financial crisis period from 2007Q3 to 2009Q2. We implement this canonical credit risk model in accordance with the *CreditGrades technical document* jointly developed by Goldman Sachs, JP Morgan, Deutsche Bank, and RiskMetrics. Our empirical study focuses on the strengths and weaknesses of the chosen risk model by analyzing the main empirical results with several complementary statistical and qualitative tests for better triangulation.

Keywords: CreditGrades; credit default swap spreads; credit risk models; default probability estimates; and financial crises.

 

Key motifs in the home-host mantra of operational risk management

We describe, discuss, and delve into the key practical policy issues around the current implementation of the Basel advanced measurement approach (AMA) to operational risk management. These issues pertain to operational risk governance, capital measurement and allocation, and future advances in operational risk transfer. Although we assess these issues from a host supervisor's perspective, the main themes have practical implications in a broader home-host context.

In terms of operational risk governance in the host regime, the local board and senior management must take an active interest in operational risk management. Several work streams such as operational loss scenario analysis, business continuity management, and external audit or other forms of independent assurance could help reinforce this active interest. Also, the significant subsidiary of a globally active bank is expected to hold an adequate amount of capital for operational risk. At the more disaggregated level, capital allocation must create sound incentives for business lines to carry out effective operational risk management processes. This capital allocation calls for the integration of the operational risk management processes into the business-as-usual teams. Furthermore, it is important to create a robust nexus between managerial pay and operational risk-adjusted return. This nexus helps strengthen the incentives for business lines to enhance the overall quality of operational risk management over time.

Future work could shine new light on the next-generation capital treatment of diversification benefits between operational risk and other risks as part of the Pillar 2 supervisory review process. This work adds value to the internal capital adequacy assessment process (ICAAP). Also, future developments should help promote operational risk transfer via insurance contracts or derivatives. These risk products could help advance the evolution of operational risk management to a new era.

Keywords: Pillar 1 operational risk management; Basel home-host harmonization; corporate governance; risk management; standalone capability; risk scorecard capital allocation; Loss Distribution Analysis (LDA); Pillar 2 internal capital adequacy assessment process (ICAAP); inter-jurisdictional capital diversification; insurance; and derivative risk transfer.

 

An algorithmic model for retail credit portfolio segmentation

Under the new Basel bank capital framework, each bank must group its retail exposures into multiple segments with homogeneous risk characteristics. The U.S. regulatory agencies believe that each bank may use its internal risk models for the loan-level risk parameter estimates such as probability of default (PD) and loss given default (LGD) to group individual exposures into the resultant segments with homogeneous risk attributes. In stark contrast to the conventional decision-tree method, we propose a new algorithmic technique for retail consumer loan portfolio segmentation. This new technique identifies the optimal number of segments, sorts the individual loan exposures into the various segments, and then leads to the minimal degree of risk heterogeneity in comparison to the baseline equal-bin and quantile-bin schemes. Furthermore, we analyze the Monte Carlo implicit asset correlation values for the retail loan segments over time to help assess the implications for bank capital measurement. The best-fit method for retail credit portfolio segmentation results in some capital relief that serves as an economic incentive for the bank to invest in this alternative segmentation. This positive outcome accords with the core principle of statistical conservatism that the financial econometrician enshrines in the Basel regulatory requirements for bank capital measurement.

Keywords: Basel risk model development; Monte Carlo simulation; asymptotic single risk factor model; credit risk; segmentation; retail mortgage segmentation; k-means cluster analysis; risk capital management; and asset correlation analysis.

 

A new Basel bank capital framework

We describe, discuss, and delve into the new Basel bank capital framework and the central bank's general approach to its practical implementation in the broader home-host context of Australia and New Zealand. Bank capital plays an important role in absorbing large financial losses, especially under severe downturn conditions. Financial regulators have an active interest in the actual quantum of bank capital and therefore set the minimum capital-adequacy requirements for banks. The new Basel bank capital regime refreshes the previous set of regulatory requirements and in turn serves as a new framework for thinking about the important role of capital in the banking system.

The primary policy objective of the new Basel bank capital framework is to increase the relative sensitivity of regulatory capital requirements to changes in the typical systemically important bank's exposure to financial risk. Specifically, marginal changes in regulatory capital-adequacy requirements should manifest in marginal changes in the typical bank's exposure to financial risk. The main types of financial risk include credit risk, operational risk, market risk, as well as procyclicality risk and strategic risk, the latter of which may or may not be part of the financial risk toolkit for practitioners. This risk modeling enhancement provides incentives for banks to upgrade their internal risk models, risk management systems, and operational processes.

The central bank is responsible for setting regulatory capital requirements for banks. For those locally-incorporated banks that have offshore banking operations, the central bank liaises closely with the relevant foreign supervisors to ensure a smooth and efficient Basel capital implementation for synchronous home-host coordination. For the purpose of this policy discussion, we pay particular attention to the major implications of risk parameter adjustment throughout the macroeconomic cycle. This insight connects macroprudential risk management and bank capital adequacy to the monetary policy sphere.

Keywords: Basel capital framework; bank capital adequacy; Pillar 1 risk parameter adjustment; default probability; loss given default; exposure at default; macroeconomic cycle; market risk; wholesale credit risk; retail credit risk; operational risk; procyclicality risk; strategic risk; legal risk; Pillar 2 supervisory review; Pillar 2 internal capital adequacy assessment process (ICAAP); Pillar 3 market disclosure.

 

Both path dependence and convergence in the modern evolution of corporate ownership around the world

We derive and develop a simple and intuitive model that shines fresh light on the relentless debate over whether corporate ownership converges to the Berle-Means modern corporation with high stock ownership dispersion. Our model takes into account the importance of both protective legal institutions and firm-specific asset arrangements. The main analytical result is that incumbent stock ownership concentration either persists or declines depending on the relative importance of these protective arrangements. Specifically, our model predicts: (a) high stock ownership dispersion in nations that impose legal limits on blockholders's clout to expropriate minority shareholder rights, and (b) high stock ownership concentration in nations that primarily rely on asset specificity as a form of investor protection. In this view, both the path-dependency and convergence theories complement each other in the broader context of corporate governance.

Our empirical analysis of international data suggests at least partial convergence toward the Berle-Means modern corporation with high stock ownership dispersion. It is thus plausible to infer the existence of path-dependent forces on corporate ownership concentration. Nevertheless, this result does not preclude the more dynamic form of functional convergence toward greater stock ownership dispersion through the general tendency of non-U.S. public firms to cross-list on the major U.S. stock exchanges. This trend introduces stringent disclosure and governance requirements to a wider set of multinational corporations. In essence, these empirical results suggest a case for the co-existence of the path-dependency and functional-convergence stories. These complementary stories arise as stable mates and represent some partial elements of truth in explaining the cross-country variation in corporate ownership and governance structures.

Keywords: corporate governance; corporate ownership; legal protection; asset specificity; path dependence; convergence; and Berle-Means modern corporation.