Major US Banks Research Articles

The stock price forecast under the failure of silicon valley bank based on the ARIMA model

The collapse of Silicon Valley Bank on March 10, 2023, had a profound impact on the stock prices of many companies in the United States. This study aims to examine the response of other banks in the US to this event by utilizing the Autoregressive Integrated Moving Average (ARIMA) model to forecast their stock prices. The research demonstrates that the ARIMA model effectively predicts the general trend of these banks' stock prices, with Root Mean Squared Error (RMSE) values below 1 for four out of six major US banks. These findings indicate that the proposed method is a promising tool for managing sudden fluctuations in stock prices, outperforming traditional linear regression models. Consequently, this research provides valuable insights for investors and financial institutions in managing and mitigating risks associated with abrupt market changes. Additionally, the study contributes to a greater understanding of the effects of bank collapses on the stock market. Overall, the research highlights the significance of incorporating advanced forecasting methods, such as ARIMA, in analyzing and predicting stock price movements in volatile market conditions.

Rentiers of the low-carbon economy? Renewable energy's extractive fiscal geographies

Progressive movements today call for transformative state-led investment in renewable energy and other climate infrastructures—in the United States, a vision that confronts inherited legacies of austerity. I argue that a significant obstacle is the neoliberal toolkit through which the US federal government subsidizes renewables, an indirect, highly opaque system of tax credits and incentives. For forty years, tax subsidies have ‘paid’ private financial players to invest in renewables, via allowing them to claim legal tax shelters against their other income. In this political economic analysis, I question, first, how US renewable energy acquired this peculiar form of public finance ‘through the tax code’, unique in the global industry. Second, I explore how the model has shaped US renewables financing, development, and ownership. I center two decisive moments: the California ‘wind rush’ in the 1980s, and the ongoing renewables boom of the last fifteen years. This history articulates financial experiments and tax sheltering scandals of the Reagan Administration with exploitation returned today in more organized (and lucrative) form, as ‘tax equity’ finance. Via tax equity, a handful of major US banks dominate financing for renewables and other politically embattled public goods. They exert a troubling ability to extract rents for their capital, gatekeep what projects get built and by whom, and stall US renewables development altogether. Today, the practice is increasingly strained by these and other problems—growing public costs, private capacity ceilings, and amplification of sectoral crises. Under Biden, it faces probable reform, but may need more comprehensive reimagination.

OSL with chips from US credit cards

Chips from credit cards issued by the major US banks have been tested with optically stimulated luminescence (OSL) as potential materials for use in emergency radiation dosimetry. The following dosimetric properties have been studied: (1) OSL decay curves from both irradiated and unirradiated samples; (2) dose-response characteristics of the OSL and values of minimum detectable doses; (3) fading of the OSL signal with time after irradiation. No OSL was detected in unexposed samples while prominent signals were observed after irradiation. It was found that the shape of the OSL signals can be fitted by a combination of an exponentially decaying function and a power function. The dose-response relationship was linear in the range 0–7 Gy if the OSL signals were measured at the same effective time after irradiation and normalized to the response to a test dose. Dose recovery tests demonstrated that the doses can be reconstructed with an accuracy ±20% if the OSL measurements are conducted within 7 days after exposure and the corresponding OSL doses are corrected for fading.

Cops, Firefighters, and Scapegoats: Anti-Money Laundering (AML) Professionals in an Era of Regulatory Bulimia

U.S. criminologists have overlooked the phenomenon of the expansion of the AML (anti-money laundering) industry. This article contributes to the literature on AML professionals in major US banks and their occupational culture through semi-structured interviews with 22 senior AML specialists. It investigates their understanding of their ambiguous role at the intersection of regulation, law enforcement and profit-making, and their perception of what effectiveness and success within their role means given competing objectives. Also probed are the interviewees' value-orientations and location amid political, economic and regulatory/legal changes and employment pressures.

Market response to syndicated loan announcements from high‐profile failed and acquiring banks during the global financial crisis

AbstractPrevious studies that have examined the impact of the 2008 financial crisis on syndicated loans have ignored potential differences between lending banks by explicitly or implicitly aggregating all lenders together and focusing on borrower characteristics. One must jointly consider both borrower and lender to fully understand the complex role of the syndicate during this period. We consider the identity of the lender, with a focus on five major US banks that failed and their five corresponding acquirers. Our results highlight the distinct roles of investment and commercial banks and facilitate an understanding of relationship and transactional‐based lending.

Modeling and monitoring risk acceptability in markets: The case of the credit default swap market

Minimal discounted distorted expectations across a range of stress levels are employed to model risk acceptability in markets. Interactions between discounting and stress levels used in measure changes are accommodated by lowering discount rates for the higher stress levels. Acceptability parameters represent a maximal and minimal discount rate, a maximal stress level and the speed of rate reduction in response to stress. An explicit model relating credit default swap (CDS) prices to default probabilities is formulated with a view to making the default risk market acceptable. Data on CDS prices and default probabilities for the six major US banks obtained from the Risk Management Institute of the National University of Singapore is employed to estimate parameters defining acceptability and the movements in market implied recovery rates. We observe that the financial crisis saw an increase in the maximal discount rate and its spread over the minimal rate along with an increase in the maximal stress level being demanded for acceptability and a stable pattern for the speed of rate adjustment through the period. The maximal rate, rate spread and stress levels have come down but with periods in the interim where they have peaked as they did in the crisis. Recovery rates have oscillated and they did fall substantially but have recovered towards 40 percent near the end of the period.

Hedging Customer Risk

Companies and academics rarely account for balancing the risk and reward in a customer portfolio of a firm. Unlike a stock portfolio where both measures are taken into account, many people tend to look only at the customers’ average profit value. Applying financial portfolio management techniques to customer management has been an under-researched topic in academic marketing literature. In this paper, we propose a methodology that allows firms to create a customer portfolio maximizing return for a level of risk. We illustrate the potential benefits by optimally weighting the customer segments based on their risk–reward impact on the firm’s overall customer portfolio. Our method can be used to assist investors in their valuation of a firm or to value an acquisition with a primary goal of increasing the customer base. We use a dataset of 1,901 customers covering seven quarters for a total of 13,307 observations of a major US bank with a market capitalization exceeding $40 billion. We show measurable potential increase in profit by implementing customer portfolio management practices.

Hedging Customer Risk

Companies and academics rarely account for balancing the risk and reward in a customer portfolio of a firm. Unlike a stock portfolio where both measures are taken into account, many people tend to look only at the customers’ average profit value. Applying financial portfolio management techniques to customer management has been an under-researched topic in academic marketing literature. In this paper, we propose a methodology that allows firms to create a customer portfolio maximizing return for a level of risk. We illustrate the potential benefits by optimally weighting the customer segments based on their risk–reward impact on the firm’s overall customer portfolio. Our method can be used to assist investors in their valuation of a firm or to value an acquisition with a primary goal of increasing the customer base. We use a dataset of 1,901 customers covering seven quarters for a total of 13,307 observations of a major US bank with a market capitalization exceeding $40 billion. We show measurable potential increase in profit by implementing customer portfolio management practices.

Why China Should Invest Its Foreign Exchange Reserves in the Major US Banks

AbstractThe subprime mortgage crisis and the resultant inflationary monetary policy in the USA have left the Chinese economy subject to four risks in particular. First, China's exports to the USA might continue to decline. Second, in the medium term, the higher US inflation rate will lead to a weak dollar, which will negatively affect China's exports. Third, in the long term, when the US Federal Reserve decreases money supply to control inflation, the US economy might enter another recession, hurting China's exports further. Fourth, China's foreign exchange reserve assets might suffer heavy losses when the US inflation rate rises. Conventional foreign exchange investment strategies are insufficient for dealing with these four risks. Investment by China in the major US banks is suggested in the present paper. This strategy would mitigate if not eliminate all four risks. China could gain considerable financial returns on investments with only moderate risk.

A framework for assessing the systemic risk of major financial institutions

In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks and forecasted asset return correlations. Importantly, using realized correlations estimated from high-frequency equity return data can significantly improve the accuracy of forecasted correlations. Our stress testing methodology, using an integrated micro–macro model, takes into account dynamic linkages between the health of major US banks and macro-financial conditions. Our results suggest that the theoretical insurance premium that would be charged to protect against losses that equal or exceed 15% of total liabilities of 12 major US financial firms stood at $110 billion in March 2008 and had a projected upper bound of $250 billion in July 2008.

The World Economy

The past few weeks have seen turmoil and turbulence in global financial markets, with 21 January seeing the sharpest one-day share price falls in many Asian and European markets since 2001, ostensibly in response to widespread fears of an imminent recession in the US. A careful examination of the available real indicators suggests that much of the recent panic in financial markets is not based on fundamentals, although the aggressive response of the Federal Reserve, with the biggest rate move since 1994 at an unscheduled meeting, gave the impression that it was privy to information that had not yet appeared in hard data, such as the imminent collapse of a major US bank. However it is also possible that the Federal Reserve acted precipitately to technical fall out from losses at Société Générale in France, which seems to have sparked much of the panic trading. The Banque de France informed the Federal Reserve of the matter in advance of their meeting scheduled for the following week. It is possible that if the Federal Reserve had waited for all the information they needed they might not have acted, and indeed they may have damaged their credibility by their precipitate action.

The subprime fiasco: Derivatives and ratings

The easy-money policies of the Federal Reserve Board (the Fed) in the early part of the decade did fuel the mortgage bubble. More important in structural terms, however, was the advocacy by the Fed and other US bank regulators of over-the-counter derivatives dealing by the major US banks. Without the wonders of structured finance, there would have been no ‘demand pull’ surge for mortgage paper starting in the 2003 period, when buy-side investors began to clamour — no, scream — for higher risk-adjusted returns.

CLASSIFYING CREDIT CARD ACCOUNTS FOR BUSINESS INTELLIGENCE AND DECISION MAKING: A MULTIPLE-CRITERIA QUADRATIC PROGRAMMING APPROACH

A major challenge in credit card portfolio management is to classify and predict credit cardholders' behaviors in a reliable precision because cardholders' behaviors are rather dynamic in nature. This is crucial for creditors because it allows them to take proactive actions and minimize charge-off and bankruptcy losses. Although the methods used in the area of credit portfolio management have improved significantly, the demand for alternative and sophisticated analytical tools is still strong. The objective of this paper is to propose a multiple criteria quadratic programming (MCQP) to classify credit card accounts for business intelligence and decision making. MCQP is intended to predict credit cardholders' behaviors from a nonlinear perspective that is justifiable because both the objective functions and constraints in credit card accounts classification may be nonlinear. Using a real-life credit card dataset from a major US bank, the MCQP method is compared with popular and similar classification methods: linear discriminant analysis, decision tree, multiple criteria linear programming, support vector machine, and neural network. The results indicate that MCQP is a promising business intelligence method in credit card portfolio management.

Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming

In credit card portfolio management, predicting the cardholder's spending behavior is a key to reduce the risk of bankruptcy. Given a set of attributes for major aspects of credit cardholders and predefined classes for spending behaviors, this paper proposes a classification model by using multiple criteria linear programming to discover behavior patterns of credit cardholders. It shows a general classification model that can theoretically handle any class-size. Then, it focuses on a typical case where the cardholders' behaviors are predefined as four classes. A dataset from a major US bank is used to demonstrate the applicability of the proposed method.

CLASSIFICATIONS OF CREDIT CARDHOLDER BEHAVIOR BY USING FUZZY LINEAR PROGRAMMING

Behavior analysis of credit cardholders is one of the main research topics in credit card portfolio management. Usually, the cardholder's behavior, especially bankruptcy, is measured by a score of aggregate attributes that describe cardholder's spending history. In real-life practice, statistics and neural networks are the major players to calculate such a score system for prediction. Recently, various multiple linear programming-based classification methods have been promoted for analyzing credit cardholders' behaviors. As a continuation of this research direction, this paper proposes a heuristic classification method by using the fuzzy linear programming (FLP) to discover the bankruptcy patterns of credit cardholders. Instead of identifying a compromise solution for the separation of credit cardholder behaviors, this approach classifies the credit cardholder behaviors by seeking a fuzzy (satisfying) solution obtained from a fuzzy linear program. In this paper, a real-life credit database from a major US bank is used for empirical study which is compared with the results of known multiple linear programming approaches.

Marketing strategies of offices of foreign banks in the changing US financial markets

US money and capital markets have changed dramatically over the past 25 years. Despite the change and uncertainty, lending activities of branches and agencies continued to expand in US markets through the early 1990s. The focus of lending by agencies and branches of foreign banks has been altered to accommodate their overall growth. The largest relative gain in loan activities of foreign banks has been in real estate and business loans. In addition, loans to other financial institutions have declined in relative terms. The change in lending activities is consistent with what has been termed a commercial‐industrial strategy, one of several unique marketing strategies that have been employed by major US banks.

Quality Control Procedures to Determine Staff Allocation in a Bank

A major US bank has determined that none of its customers should have to wait more than three minutes for service. Presents a procedure that helps management allocate tellers to prevent any customer waiting time from exceeding the three‐minute limit. Uses X¯ and s charts to monitor the average waiting time and the standard deviation of waiting times. Customer waits exceeding three minutes and customer arrivals are monitored using c charts. The approach integrates the analysis of the c charts along with that of the X¯ and s charts to determine the number of tellers necessary to maintain customer service at the desired level. The lobby operations are simulated and the procedure illustrated using operational data supplied by the bank.