Log-periodic Power Law Research Articles

Detecting market bubbles: A generalized LPPLS neural network model

To enhance bubble detection capabilities, we introduce two significant improvements to the Log-Periodic Power Law Singularity (LPPLS) model: (1) a novel fitting approach, which yields more accurate predictions of critical price distributions within a single sample window; (2) a restructured neural network approach further enhances the estimations of the probability distributions of the critical points across both time and price dimensions, and it can be fine-tuned with real-world data. The simulation and practical applications to typical asset price bubbles in cryptocurrencies, commodities, and equity indices demonstrate that our refined model, the Generalized-LPPLS Neural Network (G-LPPLS-NN), outperforms all other models we examined in terms of predictive accuracy for critical point distributions.

Stock market volatility and multi-scale positive and negative bubbles

We study whether booms and busts in the stock market of the United States (US) drives its volatility. Given this, first, we employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. We successfully detect major crashes and rallies during the weekly period from January 1973 to December 2020. Second, we utilize a nonparametric causality-in-quantiles approach to analyze the predictive impact of our bubble indicators on daily data-based weekly realized volatility (RV). This econometric framework allows us to circumvent potential misspecification due to nonlinearity and instability, rendering the results of weak causal influence derived from a linear framework invalid. The MS-LPPLS-CIs reveal strong evidence of predictability for RV over its entire conditional distribution. We observe relatively stronger impacts for the positive bubbles indicators, with our findings being robust to an alternative metric of volatility, namely squared returns, and weekly realized volatilities derived from 5 (RV5)- and 10 (RV10)-minutes interval intraday data. Furthermore, we detect evidence of predictability for RV5 and RV10 of nine other developed and emerging stock markets. In addition, we also find strong evidence of causal feedbacks from RV5 and RV10 on to the MS-LPPLS-CIs of the 10 countries considered. Finally, time-varying connectedness of the RVs of the G7 stock markets is also shown to be strongly (positively) predicted by the connectedness of the six bubbles indicators. Our findings have significant implications for investors and policymakers.

Identifying price bubbles in global carbon markets: Evidence from the SADF test, GSADF test and LPPLS method

Amid growing climate concerns, Emissions Trading Schemes (ETS) serve as key mechanisms for reducing greenhouse gas emissions through market forces. This study delves into the phenomenon of price bubbles within six major ETSs globally: the European Union Emissions Trading Scheme (EU ETS), the New Zealand Emissions Trading Scheme (NZ ETS), the California-Québec Emissions Trading Scheme (CQ ETS), the South Korea Emissions Trading Schemes (K-ETS), the China Emissions Trading Scheme (C-ETS), and the United Kingdom Emissions Trading Schemes (UK ETS). Employing the sup augmented Dickey-Fuller (SADF) test, generalized sup ADF (GSADF) test, and log-periodic power law singularity (LPPLS) model, our analysis aims to uncover the dynamics and implications of these bubbles. Our findings reveal varied patterns of price bubbles across the schemes, with the EU ETS showing the highest frequency and the NZ ETS exhibiting the longest durations. The period of 2021 and 2022 marked a significant increase in bubble occurrences across all ETSs, suggesting the influence of post-COVID-19 economic recovery and geopolitical tensions on market stability. These bubbles are attributed to factors including carbon market policies, macroeconomic conditions, energy price fluctuations, and market uncertainties. The study offers valuable insights for compliance firms, policymakers, and investors on navigating carbon market volatility. Understanding bubble dynamics can aid in formulating more effective emission reduction strategies, carbon pricing policies, and investment decisions. This research underscores the importance of integrating monetary policy considerations with environmental objectives within ETS frameworks, paving the way for future research on predictive bubble detection and the development of robust carbon market regulations.

Does every cloud (bubble) have a silver lining? An investigation of ESG financial markets

This paper investigates the presence of financial bubbles in the environmentally friendly investments captured by the ESG markets. By using the log-periodic power law singularity framework, we identified several periods of positive and negative bubbles in the short, medium, and long term. Moreover, we examined the relationship between ESG attention sentiment and financial bubbles. We found an asymmetric effect of ESG sentiment on financial bubbles, i.e., increasing positive and decreasing negative bubbles. Our empirical results provide valuable insights into the stability of environmentally friendly markets, which help risk managers and policymakers respond appropriately to financial and social bubbles.

Monetary policy uncertainty and the price bubbles in energy markets

We examine the relationship between the monetary policy uncertainty (MPU) and the price bubbles in U.S. oil futures, including WTI crude oil future, heating oil future, and gasoline future. The Log Periodic Power Law Singularity (LPPLS) model is firstly used to analyze and validate the price bubbles of U.S. oil futures. We find that (1) the timing of local peaks of MPU closely aligns with the occurrence of price bubbles in U.S. oil futures, (2) MPU is significantly correlated with crude oil future price bubble but not with heating oil future (or gasoline future) price bubble, and MPU significantly amplify the magnitude of price bubble risk for crude oil future, (3) MPU is an effective set of factors for machine-learning based identification of all three U.S. oil future price bubbles in the non-linear model, even though the impact of MPU on heating oil (or gasoline) future price bubble is not significant in logit regression as the linear model. Collectively, our findings highlight the significance of MPU in relation to price bubbles in U.S. oil futures, offering new insights for investors and policymakers to explore the (non)-linear relationship between MPU and price bubbles in energy markets.

Detection of financial bubbles using a log‐periodic power law singularity (LPPLS) model

AbstractThis article provides a systematic review of the theoretical and empirical academic literature on the development and extension of the log‐periodic power law singularity (LPPLS) model, which is also known as the Johansen–Ledoit–Sornette (JLS) model or log‐periodic power law (LPPL) model. Developed at the interface of financial economics, behavioral finance and statistical physics, the LPPLS model provides a flexible and quantitative framework for detecting financial bubbles and crashes by capturing two salient empirical characteristics of price trajectories in speculative bubble regimes: the faster‐than‐exponential growth of price leading to unsustainable growth ending with a finite crash‐time and the accelerating log‐periodic oscillations. We also demonstrate the LPPLS model by detecting the recent bubble status of the S&P 500 index between April 2020 and December 2022, during which the S&P 500 index reaches its all‐time peak at the end of 2021. We find that the strong corrections of the S&P 500 index starting from January 2022 stem from the increasingly systemic instability of the stock market itself, while the well‐known external shocks, such as the decades‐high inflation, aggressive monetary policy tightening by the Federal Reserve, and the impact of the Russia/Ukraine war, may serve as sparks.This article is categorized under: Applications of Computational Statistics > Computational Finance Algorithms and Computational Methods > Computational Complexity Statistical Models > Nonlinear Models

Stock market bubbles and the realized volatility of oil price returns

Using monthly data for the G7 countries from 1973 to 2020, we study whether stock market bubbles help to forecast out-of-sample the realized volatility of oil price returns. We use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. First, we successfully detect major crashes and rallies using the MS-LPPLS-CIs. Having established the relevance of the bubbles indicators, and given the large number of them, we use widely-studied shrinkage (Lasso, elastic net, ridge regression) approaches to estimate our forecasting models. We find that stock market bubbles have predictive value for realized volatility at a short to intermediate forecast horizon. The number of bubble predictors included in the penalized forecasting models tend to increase in the forecast horizon. We obtain our main finding for the various types of stock market bubbles, and for good and bad realized volatilities.

Extreme events, economic uncertainty and speculation on occurrences of price bubbles in crude oil futures

This paper examines the impacts of extreme events, economic uncertainty and speculation on price bubbles in crude oil futures. For better forecast and estimate the positive/negative price bubbles of crude oil futures, the log-periodic power law singularity confidence multi-scale indicators application is used. The panel probit model examines impacts of extreme events, economic uncertainty and speculation on price bubbles in crude oil. The results show that the recent extreme events including Corona Virus Disease 2019 and Russia-Ukraine war significantly affect both positive/negative bubbles in crude oil futures. Measures of economic uncertainty significantly affect both positive/negative bubbles in the crude oil. However, the results find that the speculation significantly affect the positive bubbles and does not significantly affect the negative bubbles. Overall, extreme events, economic uncertainty, and speculation can all contribute to the occurrences of price bubbles in crude oil futures markets. This study provides new insights for the policy makers and market participants to monitor the occurrence of price bubbles.

Detection of financial bubbles using a log-periodic power law singularity (LPPLS) model

Detection of financial bubbles using a log-periodic power law singularity (LPPLS) model

Predicting stock market crashes on the African stock markets: evidence from log-periodic power law model

PurposeThis study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.Design/methodology/approachOver the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.FindingsBy minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.Practical implicationsThe adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.Originality/valueThis study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

Why topological data analysis detects financial bubbles?

We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price.

The Construction of Judgment Model for Cultivating Intelligent Accounting Professionals in Universities Based on Logarithmic Periodic Power Law Model

Abstract The talent training evaluation model not only helps to evaluate the talent itself but also provides feedback on the content of the talent training evaluation. Therefore, this paper establishes an efficient and intelligent talent training evaluation model for accounting professionals based on the logarithmic cycle power law model. The main content of talent training evaluation is set as general knowledge skills, professional thinking, and values. The log-periodic power-law model and the least squares method are combined to reduce the dimensionality of the nonlinear parameters of the judging content and to quantify the judging of intelligent accounting professional talent training in universities, which is convenient for the calculation of linear functions. With the help of log-periodic power-law oscillation to prove that talent training is changing in a cyclical pattern, the feasibility of its prediction is demonstrated. The study shows that the talent cultivation judgment model constructed based on the log-periodic power-law model is very accurate, especially in talent cultivation value judgment prediction. The model achieves zero error in the prediction of some data, and the maximum error between prediction and actual is only 6%. In the judgment of general knowledge and skill cultivation, the maximum error between the prediction and the actual score of the model is no more than 2 points. This shows that the talent development evaluation model based on the log-periodic power law model can make accurate predictions of talent development evaluation.

Positive and negative price bubbles of Chinese agricultural commodity futures

This study examines the predictability of positive and negative price bubbles of the Chinese agricultural commodity futures with the log-periodic power law singularity (LPPLS) confidence multi-scale indicators application. To measure the bubble risks of these commodity futures, the duration, the frequency and the magnitude of the price bubble are adopted as the evaluation indicators. The results found that the Chinese agricultural commodity futures with higher bubble risks are soybean, soybean oil, soybean meal, white sugar, cotton and corn futures. Furthermore, the price bubbles of Chinese agricultural commodity futures are affected by the investor sentiment and the overall degree of macroeconomic factors. Economic growth, money supply, and inflation have positive effects on the occurrences of the positive bubbles, while currency appreciation and interest rate have negative effects and vice versa on the occurrences of the negative bubbles. Financialization has positive effects on both occurrences of the positive and negative bubbles. Overall, this study provides new insights for the policy makers and market participants to monitor the occurrence of price bubbles.

Investor sentiment and multi-scale positive and negative stock market bubbles in a panel of G7 countries

Firstly, we use the log-periodic power law singularity multi-scale confidence indicator (LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock market indices of the G7 countries. Secondly, we apply heterogeneous coefficients panel data-based regressions to analyse the impact of investor sentiment, proxied by business and consumer confidence indicators, on the indicators of bubbles of the G7. Controlling for the impacts of output growth, inflation, monetary policy, stock market volatility, and growth in trading volumes, we find that investor sentiment increases the positive and reduces the negative LPPLS-CIs, primarily at the medium- and long-term scales for the G7, considered together, with the result being driven by at least five of the seven countries. Our results have important implications for both investors and policymakers, as the collapse (improvement) of investor sentiment can lead to a crash (recovery) in a bull (bear) market.

US monetary policy and BRICS stock market bubbles

We use the multi-scale Log-Periodic Power Law Singularity (LPPLS) confidence indicator approach to detect both positive and negative bubbles at short-, medium- and long-run for the stock markets of the BRICS countries. Then, we utilize impulse responses obtained from the local projection method (LPM) framework to capture the effect of US monetary policy shocks on the BRICS bloc equity market. The effect of these shocks on the bubble indicators for each country is limited, with a strong positive impact observed under the medium-term negative bubble indicator of Brazil, China and South Africa. Given the findings, associated policy implications are discussed.

Asset Price Bubble under Behaviroral Finance Theory: Based on Log-Periodic Power Law Model

The asset price bubble problem is not only the most concerned topic in the financial circle, but also one of the most important research topics in the financial circle. In history, every time asset prices skyrocketed, bubbles were accumulated, and every asset price crash resulted in a massive shrinking of wealth, bankruptcy of enterprises, and economic recession. This paper is based on the theory of investor behavioral bias in behavioral finance theory, and is based on the log-periodic power law (LPPL) theory of iron ore futures, apple futures, coke futures and stock market indices that is widely used in Chinese financial markets. The market index and bitcoin price are empirically analyzed through the process of bubble accumulation, and the stock market index of China's stock market and the index of China's capital market are predicted and analyzed based on LPPL theory.

Bubble in Carbon Credits during COVID-19: Financial Instability or Positive Impact (“Minsky” or “Social”)?

Incentivizing businesses to lower carbon emissions and trade back excess carbon allowances paved the way for rapid growth in carbon credit ETFs. The use of carbon allowances as a hedging alternative fueled this rally further, causing a shift to speculation and forming repetitive bubbles. Speculative bubbles are born from euphoria, yet, they are relatively predictable, provided their pattern matches the log periodic power law (LPPL) with specific stylized facts. A “Minsky moment” identifies a clear speculative bubble as a signal of financial system instability, while a “Social bubble” is regarded as relatively positive, increasing in the long run, infrastructure spending and development. The aim of this paper is to investigate whether various carbon credit bubbles during the pandemic period caused financial instability or had a positive impact (“Minsky” or “Social”). Particularly, we investigate the carbon credit bubble behavior in the ETF prices of KRBN, GRN (Global Carbon Credit tracking ETFs), and the SOLCARBT index during the COVID-19 pandemic period by adopting the log-periodic power law model (LPPL) methodology, which has been widely used, over the past decade, for detecting bubbles and crashes in various markets. In conclusion, these bubbles are social and propelled by the newfound interest in carbon credit trading, for obvious reasons.

Simulation of Financial Market Performance and Algorithmic Economic Model based on Complex Network

At present, traditional macro-financial models such as random walk model and log-periodic power law model in academia cannot explain the stylized characteristics of financial markets. So, we propose a microscopic model that produces stylized characteristics of real financial markets. The model integrates the herding effect, the non-linear relationship of investors and the non-linear structure of the system very well. The research results show that the financial market model established in this paper can simulate most of the characteristics of the real financial market price time series relatively accurately. The model helps to understand the internal operating mechanism of the financial system and provides some basis and reference for the establishment of financial market forecasting models.

Deconstruction of the Green Bubble during COVID-19 International Evidence

Bubbles are usually chaotic but can be predictable, provided their formation matches the log periodic power law (LPPL) with unique stylized facts. We investigated Green Bubble behaviour in the stock prices of a selection of stocks during the COVID-19 pandemic, namely, those with the highest market capitalization from a basket of North American and European green energy or clean tech companies and the S&P Global Clean Energy Index. Moreover, the biggest Exchange Traded Fund (TAN) by market capitalization was also considered. The examined period is from 31 December 2019 to 11 October 2021, during which we detected 35 Green Bubbles. All of these followed the LPPL signature while calibrated through the 2013 reformulated LPPL model. In addition, the average drawdown emerged as four times that of the regular S&P-500 stock index (108% vs. 27%) under stressed conditions, such as the COVID-19 pandemic (stylized fact). Finally, the aftermaths of Green Bubbles, unlike regular bubbles, are not destructive, as these bubbles increase economic activity and infrastructure spending and are hence beneficial for holistic growth (described as Social Bubble Hypothesis). We document that there are benefits in adapting greener and more sustainable business models in energy production. Green and sustainable finance offers benefits and opportunities for stock exchanges, especially for energy stocks. As a result, many businesses are focusing on sustainability and adopting an eco-friendly business model, which helps the environment, helps sustainability and attracts investors.

Exploring the Endogenous Nature of Meme Stocks Using the Log-Periodic Power Law Model and Confidence Indicator

This study examined the endogenous nature of negative bubbles forming in meme stocks with the Log-Periodic Power Law (LPPL) Confidence Indicator (CI). A meme stock is a stock that has gained a significant amount of attention on a large social media platform such as Yahoo! or Reddit. This study examined four meme stocks including Tesla, Inc. (TSLA), GameStop Corp. (GME), Koss Corporation (KOSS), and AMC Entertainment Holdings Inc (AMC). The CI was able to detect numerous bubbles forming in meme stocks, but had difficulty in significantly predicting social media-induced exogenous rallies. This may have been due to price movements affected by external causes such as short squeezes. However, the model did provide proof for the formation of previous bubbles that could have been a catalyst for the meme stocks rallies. This study outlines the real unpredictability of many black-swan events, and further studies could be done examining exogenous bubbles.