Weak Return Research Articles

Deep learning assisted exponential waveform decomposition for bathymetric LiDAR

Abstract. The processing of bathymetric LiDAR waveforms is an important task, as it provides range and radiometric information to determine the precise location of water surface and bottom, and other characteristics like amplitude. The exponential waveform decomposition proved to be an effective algorithm for bathymetric LiDAR waveforms processing, however, it heavily relies on the high-quality initial estimates of the model parameters. This paper proposes to make use of deep learning to obtain the initial values directly from the input received waveforms without any hand-crafted features and prior-knowledges. Additionally, to provide training samples, we presents a method to create the synthetic bathymetric LiDAR waveforms by simulating of the backscatter cross function returned from water bodies. Two networks with different sensitivities of weak signals were trained by these synthetic waveforms, and used to estimate the initial values of the model parameters, a least square optimization follows up to obtain the final waveform decomposition result. This deep learning assisted exponential waveform decomposition method is applied to the real waveforms acquired by RIEGL VQ-840-G. The results show that estimations with the help of deep learning is less influenced by the intermediate peaks backscattered from objects and particles in water, producing a cleaner point cloud with less isolated points below water surface than the original exponential waveform decomposition. Moreover, the proposed sensitive DL-XDC is even able to detect some very weak bottom returns with low SNR.

Assessment of Panorama Photogrammetry as a Tool for Long-Range Deformation Monitoring.

This study investigates panorama photogrammetry (PPh) as a potential method to collect massive 3D information for long-range deformation monitoring. Particularly, this study focuses on areas with measuring restrictions, i.e., inaccessible objects and distances above 0.6 km. Under these particular conditions, geodetic techniques based on Electromagnetic Distance Meters (EDMs) or Total Stations (TSs) can provide coordinates with a precision better than 1 cm, but only for a limited number of discrete points. For mass capture, Terrestrial Laser Scanning (TLS) is normally the preferred solution, but long-range instruments are expensive, and drawbacks such as weak return signals and non-automatic target recognition appear. As an alternative, PPh is investigated in the well-controlled area of La Muela in Cortes de Pallas, where images are automatically captured from geodetic pillars using a GigaPan device, processed, and then rigorously compared to TLS point clouds. The results obtained after integrating both techniques into a high-accuracy geodetic reference frame show that PPh and TLS provide similar precision to within approximately 4 cm in the range of 0.6-1.0 km. Therefore, considering cost-effectiveness and ease of use, the proposed method can be considered a low-cost alternative to TLS for long-range deformation monitoring.

Integration of Different Density UAV Lidar and Single Beam Echo Sounder (SBES) for River and Riparian Area Digital Terrain Model (DTM) Construction

Rivers and riparian areas are vital components of ecosystems, but accurately modeling their terrain presents challenges, especially in detecting the river surface. This paper proposes an integrated approach that combines UAV LiDAR and Single Beam Echo Sounder (SBES) data to construct a Digital Terrain Model (DTM) of river and riparian areas. The objective is to overcome the limitations posed by water, which absorbs near-infrared laser energy, resulting in weak or absent LiDAR returns. Different UAV LiDAR densities were examined to determine the optimal configuration for capturing riparian areas. Evaluation of the results utilized various metrics, including root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean bias error (MBE), and correlation coefficient (CC). Three ground filtering methods were implemented and assessed: morphological filters, adaptive triangulated irregular network (ATIN) filtering, and above-ground level (AGL) filtering. Among the evaluated methods, the DTM constructed using ATIN with an 80-meter flight configuration yielded the most accurate results. It achieved an RMSE of 0.18m, an MSE of 0.03m, an MAE of 0.17m, an MBE of 9.08m, and a CC of 1.00. Comparatively, other methods exhibited higher error values and lower correlation coefficients. The findings highlight the efficacy of ATIN filtering in conjunction with an 80-meter UAV LiDAR flight for obtaining reliable DTMs of river and riparian areas. This approach demonstrates significant improvement in accuracy, particularly in terms of RMSE and MSE. The derived DTM can be a valuable tool for safeguarding and managing these critical ecosystems. In summary, this paper successfully addresses the challenge of modeling river and riparian terrains by integrating UAV LiDAR and SBES data. By employing ATIN filtering with an 80-meter flight configuration, the study achieves a highly accurate DTM. By employing ATIN filtering with an 80-meter flight configuration, the study achieves a precise, high DTM with minimal error. The developed model contributes to protecting and preserving river and riparian ecosystems.

Small target detection in sea clutter using dominant clutter tree based on anomaly detection framework

It is difficult for maritime high-resolution radars to realize the small target detection in sea clutter, due to weak target returns and complicated clutter characteristics. Cooperation of multiple features is a recognized way to distinguish target returns from clutter. Therefore, it becomes crucial to build a detector, a special classifier, with unbalanced training samples, i.e., ergodic clutter versus non-ergodic target samples. In this paper, a preferential decision tree (pre-decision tree) with the oblique stopping criterion is proposed, where a preferential Gini index (pre-Gini index) is defined to replace the Gini index and considers rigorous false alarm rates and tolerable missed probabilities for radar target detection. Then, an improved pruning is added to the pre-decision tree to generate a dominant clutter tree, which can accurately control the false alarm rate. The two-step decision is based on the anomaly detection framework and solves the unbalance of the training samples. The proposed method can work in the high-dimensional space directly, and its decision only involves linear operations. The experimental results on the recognized IPIX and CSIR databases illustrate that the proposed method performs well among the available feature-based detectors.

Connectedness and portfolio hedging between NFTs segments, American stocks and cryptocurrencies Nexus

The paper examines the dynamic spillover and hedging effectiveness between five main segments of NFTs, which are Collectibles, Art, Game, Metaverse, and Utility, and the other asset classes namely Bitcoin and the American Stocks (S&P500). The study sample covers the period from April 27, 2018 to September 15, 2022. Using a Time-Varying connectedness approach through the TVP-VAR model and inspired by the Diebold and Yilmaz spillover index, the results show weak dynamic return spillovers between NFTs and the other assets, indicating that these new digital assets are still relatively decoupled from traditional asset and Bitcoin. We find also that Bitcoin is a major transmitter of spillover whereas Collectibles, Utility and S&P500 are net recipients of spillovers. Using the DCC-GARCH model, we extract the optimal weights, hedge ratios, and hedging effectiveness for the pairwise portfolios composed of S&P500/NFTs and Bitcoin/NFTs. The results indicate that investors and portfolio managers should consider adding NFTs in their portfolios of either S&P500 or Bitcoin to achieve diversification benefits. Finally, for Robustness Checks, we forecast the performance of the hedged versus the unhedged portfolios using the Long Short-Term Memory (LSTM) networks. Our findings confirm almost the results of the hedging effectiveness of NFTs and stem for the superiority of metaverse among these assets to serve as a perfect hedge.

WOMEN AND ECONOMIC DECISION MAKING (ANTHROPOLOGICAL STUDY)

Ideas that do not consider women to have a significant role in the economic life of any society have diminished, and any country that does not believe that women are half of society and works on equality between men and women leads to weak returns that can be reaped by that economy, and weak growth rates. Many countries have excluded women from economic life and prevented them from carrying out any activity in society because of old habits that paralyzed the economy's ability to rise and grow. There is no doubt that women's participation in economic activities will theoretically contribute to more growth and advancement of economies undisputedly. In the process, women still face many difficulties because of the systems and culture, especially the difficulty of obtaining money in their societies, as well as their inability to make economic decisions related to buying and selling due to societal restrictions and customs that reduce the importance of women’s involvement in work, as empowering women in the economy and bridging gaps Gender in the world of work is essential, and women's economic empowerment includes their ability to participate on an equal footing in existing markets, and meaningful participation in economic decisionmaking at all levels from the family to international institutions, and aims to promote women's economic empowerment to increase productivity, economic diversity, social development, and equality in Income between the two sexes. Women's access to education has a fundamental role in empowering them economically, as well It is designed to enable them to keep pace with the rapid technological and digital transformations, and it also helps them obtain prestigious jobs in institutions and creates new opportunities for them, which reflects positively on the country’s economy, just as without economic development, lasting peace will not be achieved, and investing in women’s economic empowerment generates gains Short and long-term social and enhances women's participation in decision-making

Insurance Company Financial Performance Analysis

This study intends to examine the financial performance of insurance companies listed on the Indonesia Stock Exchange (IDX) between 2016 and 2019. This study employs the liquidity ratio, the profitability ratio, and the solvency ratio as indicators of financial performance. The sample comprises of thirteen insurance companies that are listed on the IDX and go public. The results revealed multiple findings. First, the liquidity ratio, which consists of the current ratio and the fast ratio, demonstrates fluctuations, particularly in the calculation of the current ratio. The maximum value for insurance companies with the AHAP code is 772 percent, which is included in the excellent criteria. In contrast, the insurance company with the code AMAG has the lowest value, 2%, which falls under the unfavorable criteria. The fast ratio also demonstrates positive results, with insurance companies having values greater than 1, indicating the availability of current assets to cover liabilities. The second variable is the solvency ratio, which comprises the total debt to equity ratio and the total debt to asset ratio. The insurance companies with the ASDM code have the highest total debt to equity ratio, which is 50%, indicating a solid equity-based return rate. Meanwhile, insurance companies with the AHAP code have the lowest value, 2%, indicating a weak equity-based return rate. The total debt to asset ratio is unfavorable, with insurance companies with the ASDM code having the highest value (83%) and insurance companies with the AHAP code having the lowest value (4%). Thirdly, return on assets (ROA) is used to compute the profitability ratio. ROA also fluctuates. Insurance companies with the AHAP code have the highest value, 5403%, which demonstrates the company's ability to generate exceptional returns on total assets. In contrast, insurance companies with the codes ABDA, AHAP, and AMAG have the lowest value, 6%, indicating a healthy rate of return on profits based on total assets.

Differential absorption lidar data acquisition and control system for remote detection of trace chemicals including methane and thiodiglycol.

We report the design and development of a data acquisition and control system for high-speed acquisition of weak, backscattered differential signals and synchronized sequential operation of all subsystems of a tunable, mid-infrared Differential Absorption Lidar (DIAL) system. The presence of a low-level concentration of chemical species results in weak differential return signals. The differential signal also varies dynamically with respect to background atmospheric conditions. The challenge is to measure this low level of differential signal with high resolution and also control the sequence of operation of subsystems such as lasers and scanners for real time testing and evaluation in open field conditions. The concentration spread of the chemical species varies rapidly with distance. In order to capture this spatial variation, the lidar signal should be sampled and digitized at a high sampling rate. A customized Peripheral Component Interconnect based data acquisition of a 12-bit resolution, 30 Mega samples per second sampling rate, and an industrial personal computer-based control system has been realized. Detection algorithms and the firing sequence of the laser have been developed indigenously and implemented in the LabVIEW platform. The developed graphical user interface has various modes of operation as per user requirement and is capable of executing automatic operations for the developed DIAL system in order to detect and identify the chemicals. The performance has been evaluated by detecting the chemical species thiodiglycol at 800m using 3190 and 3300nm (online and offline wavelengths) with a differential cross-section of 2.5 × 10-23m2. Similarly, methane has been detected and quantified with concentration of 2.2 ppm up to 300m using 3316 and 3326nm (online and offline wavelengths, respectively).

The extreme return connectedness between Sukuk and green bonds and their determinants and consequences for investors

This study aimed to investigate the return connectedness between Sukuk and green bonds at the middle, left and right tail using the new quantile-based connectivity methodology from Ando et al. (2018). We find that the average level of connectedness estimated at the mean/median is lower than that estimated at the left and right quantiles. Therefore, return connectedness between Sukuk and green links is higher in the left and right tails, indicating that the application of the mean-based connectivity measure is inappropriate. Next, we show that the connectedness of returns varies over time but varies less in the tails. In particular, the dynamic connectivity analysis indicates that the COVID-19 pandemic has significantly impacted the Sukuk and green bond markets. The Return connectedness driver's analysis shows the importance of macroeconomic conditions, particularly in the middle and lower quintiles. The US dollar bodes well positively for both bears and bulls, while uncertainty in equity markets amplifies return spillovers in the lower quintile. Moreover, the weak return spillovers between Sukuk and green bonds indicates that there is indeed an opportunity for optimal asset allocation. The highest hedging efficiency can be achieved by taking short positions in US Green Bonds.

Salam As Banking Financing for Agriculture in Developing Countries: Lessons From Sudan

The objective of this study is to measure the impact of commercial bank financing via Salam mode of finance on the Sudanese agricultural production and determine whether the Salam mode is suitable for financing agriculture or not. For the purpose of the analysis, the ordinary least squares method (OLS) was applied to a log - linear form of a simple regression model with a time series data on Sudan’s total annual agricultural production as a dependent variable and the commercial banks total annual financing via Salam for explanatory variable. The empirical results indicated that Salam mode of finance is appropriate for financing agriculture in Sudan due to its significant effects on agricultural production with a significant and positive relationship. In addition, agricultural sector highly contributed to the country economy during the study period and highly positively elastic to bank financing. However, it is hindered by heavy direct and indirect taxes, high marketing cost, long distance with poor rural infrastructure and weak agricultural returns. Therefore, relevant policy implications for both agriculture and bank financing are required. Hence, results of study suggest that banks should increase the provision for Salam mode for high agricultural production, reduce tax burdens, renovate the infrastructure, and facilitate financing procedures to agricultural sector.

Wavelength modulation‐based active laser heterodyne spectroscopy for standoff gas detection

AbstractThe wavelength modulation‐active laser heterodyne spectroscopy (WM‐ALHS), which introduces wavelength modulation technique into active laser heterodyne spectroscopy, is reported to enhance the gas standoff detection capability of weak return optical power. The ALHS gas standoff detection system is developed using a 1.65 μm butterfly packaged distributed feedback laser with tail fiber and an all‐fiber structure, which successfully detects methane concentrations at a distance of 35 cm using an aluminum plate as the reflective surface. Allan variance analysis shows that the system has good stability, and the detection limit at 29.2 s integration time is 0.84 ppm m. Standoff detection experiments with direct absorption‐active laser heterodyne spectroscopy (DA‐ALHS) and wavelength modulation spectroscopy (WMS) are carried out on the basis of the WM‐ALHS system and the detection limits of 4.49 and 1.64 ppm m were achieved within integration times of 18.6 and 29 s for DA‐ALHS and WMS, respectively. The superiority of the WM‐ALHS method compared to these two spectral detection methods is demonstrated. Furthermore, the feasibility of applying WM‐ALHS to gas standoff detection has been verified, laying the foundation for the development of this spectroscopic technology.

Volatility Spillovers and Nexus across Oil, Gold, and Stock European Markets

This paper utilises a trivariate VAR-BEKK-GARCH model to investigate the dynamic relationships between global oil price, gold price, and European stock markets. This paper observes weak return spillover effects from the oil market to 6 European stock markets (Netherlands, Lithuania, Portugal, Czech Republic, Romania, and Slovenia) and from gold to Iceland, while there is no evidence of return spillovers from stock markets to oil and gold. The non-existence of return linkages between gold and stock (oil) suggests that the gold market plays a haven role. With reference to volatility spillovers, the results show obvious asymmetric bidirectional volatility interaction between the European stock markets and the global oil/gold markets. Stronger shock and volatility contagions from the European stock market to both oil and gold markets are observed compared with the opposite direction. For the volatility nexus between oil and gold, weak and moderate evidence of shock and volatility transmission from gold to oil markets is reported. Additionally, the study documents important and effective empirical implications for portfolio management and investment hedge strategies: firstly, adding European stock markets to a diversified oil/gold portfolio can achieve the expected returns while reducing risk; and secondly, the European investors can use the gold and oil markets to hedge against their stock market portfolio.

Long distance high resolution FMCW laser ranging with phase noise compensation and 2D signal processing.

A long distance high resolution frequency-modulated continuous wave (FMCW) laser rangefinder with phase noise compensation and two-dimensional (2D) data processing skills is developed. Range-finding ladar consists of a continuously chirped laser source, an auxiliary reference interferometer, and a monostatic optical transceiver for target illumination and return photon collection. To extend the range unambiguity and lower the electronic processing bandwidth, a two-step laser frequency chirping scheme is adopted, where a long pulse width, small frequency bandwidth laser chirping signal are used in step 1 for coarse distance estimation, and a short pulse width and large frequency bandwidth laser chirping signal are applied afterwards for step 2 high resolution distance realization. An auxiliary reference interferometer is to record the phase noise originated from the laser source to compensate for phase errors induced in the target return photons. The 2D data processing skill helps to coherently sum up all the phase noise removed echo photons to achieve high resolution range peak extraction with high detection sensitivity. Experimental demonstration shows that the proposed FMCW ladar at 1550 nm wavelength with a laser chirping bandwidth of 10 GHz and electronic processing bandwidth of 200 MHz can measure a corner cube test target in an outdoor atmospheric environment, and the measurement results are 12013.905 m with a 2.4 cm range resolution under strong return photon levels and 12013.920 m with a 2.5 cm range resolution under weak return photon levels.

Emerging Market Investing: A Multi-Asset, Granular, and Dynamic Portfolio Approach

Investors seldom include emerging markets in their strategic asset allocations. The authors argue that much of this skepticism is due to a period of weak returns, exacerbated by the suboptimal construction of the traditional market-cap benchmarks. Such indexes concentrate risk in single countries, are inconsistent across asset classes, and do not reflect the fundamentals of the underlying economies. Hence, the authors propose an asset allocation framework comprising two modules. The first module constructs a portfolio with balanced risk targeted across regions, countries, and asset classes. By geographically balancing risk, the portfolio is less vulnerable to the idiosyncratic shocks prevalent in emerging markets. Moreover, the resulting portfolio overweights lower-volatility bond markets, which not only exhibit higher risk-adjusted returns but are also less correlated with riskier assets. The second module introduces tilts based on expected returns within a tracking error budget. The authors demonstrate the benefit of incorporating expected return assumptions via systematic value- and carry-based tilting.

Detection of Sea-Surface Small Targets Masked by Range Sidelobes of Large Objects

Many high-resolution maritime radars for sea-surface small target detection transmit pulses with large time-bandwidth-products to increase transmitter power and to use the pulse compression technique for high range resolution. On the sea surface, besides small targets of interest, there exist many uninteresting large objects such as big ships, reefs, and offshore platforms for oil production. Large objects have too strong radar returns and their range sidelobes often mask wide range intervals. In these range intervals, masked small targets are difficult to be detected directly. In this article, a complete scheme is proposed to find small targets masked by range sidelobes of large objects in high-resolution maritime radars. In this scheme, high-precision reconstruction of radar returns of large objects is crucial because tiny reconstruction errors are enough to mask weak returns of small targets of interest. An algorithm using the linear programming and oversampled high-resolution range profile model of a large object is given to obtain high-precision reconstruction of the strong radar returns of a large object. From residual radar returns removed the reconstructed strong returns, a near-optimal coherent detector under sea clutter modeled by the spherically invariant random vector with inverse Gamma texture plus colored Gaussian noise is constructed to find masked small targets. Simulated experiments and real measured radar data are used to verify the effectiveness of the proposed scheme and method and reveal their limitations. The results show that the proposed scheme and method are all effective unless small targets fall into the range mainlobe regions of large objects.

Static and dynamic connectedness between NFTs, Defi and other assets: Portfolio implication

The paper examines the return and volatility transmission between NFTs, Defi assets, and other assets (oil, gold, Bitcoin, and S&P 500) using the TVP-VAR framework. The results report weak static return and volatility spillovers between NFTs and Defi assets and selected markets, showing that these new digital assets are still relatively decoupled from traditional asset classes. Bitcoin, oil, and half of the NFTs and Defi assets are net transmitters of return and volatility spillovers, whereas rest of the markets are net recipients of spillovers. Our findings show that the dynamic return and volatility connectedness become higher during the initial phase of the COVID-19 pandemic and the cryptocurrency bubble of 2021. We also compute the static and dynamic optimal weights, hedge ratios, and hedging effectiveness for the portfolios of NFTs/other asset and Defi asset/other asset and show that investors and portfolio managers should consider adding NFTs and Defi assets in their portfolios of gold, oil, and stock markets to achieve diversification benefits.

Practical Applications of The Outlook for Endowment and Pension Funds

For decades, a 60/40 equity/bond allocation has been the favored allocation method for endowment and pension fund managers. In <ext-link><italic><bold>The Outlook for Endowment and Pension Funds</bold></italic></ext-link>, from the Summer 2021 issue of <italic><bold>The Journal of Wealth Management</bold></italic>, <bold>Haim Mozes</bold>, a professor of accounting at <bold>Fordham University’s Gabelli School of Business,</bold> and <bold>John Steffens</bold>, a founder and managing member of <bold>Spring Mountain Capital</bold>, formulate an outlook for the future performance of the 60/40 allocation. Mozes and Steffens identify the economic conditions that drove the 60/40 allocation’s past success and then forecast future economic scenarios. Most significant to the future performance of endowment and pension funds is the possible scenario of continued low bond yields coupled with low economic growth. In that scenario, low bond yields would produce weak returns on bonds while weak economic growth would produce poor returns on equities; the overall performance of the 60/40 allocation would be weak. The authors assess the strategies that funds are considering in order to meet their target objectives in the event of such a scenario, including increasing allocations to public equities and other risky assets.

Human Versus Machine: A Comparison of Robo-Analyst and Traditional Research Analyst Investment Recommendations

ABSTRACTWe provide the first comprehensive analysis of the properties of investment recommendations generated by “Robo-Analysts,” which are human analyst-assisted computer programs conducting automated research analysis. Our results indicate that Robo-Analyst recommendations differ from those produced by traditional “human” research analysts across several important dimensions. First, Robo-Analysts produce a more balanced distribution of buy, hold, and sell recommendations than do human analysts and are less likely to recommend “glamour” stocks and firms with prospective investment banking business. Second, automation allows Robo-Analysts to revise their recommendations more frequently than human analysts and incorporate information from complex periodic filings. Third, while Robo-Analysts' recommendations exhibit weak short-window return reactions, they have long-term investment value. Specifically, portfolios formed based on the buy recommendations of Robo-Analysts significantly outperform those of human analysts. Overall, our results suggest that automation in the sell-side research industry can benefit investors.JEL Classifications: G14; G24.

Adaptive Wavelet Threshold Denoising for Bathymetric Laser Full-Waveforms With Weak Bottom Returns

Wavelet threshold denoising with different threshold selection rules (TSRs) were used to reduce random noise (RN) in bathymetric laser full-waveforms. A nonreasonable threshold used for denoising can result in over-smoothing or under-smoothing of the signal and easily remove details of weak bottom return (BR). A unique and optimal TSR for all bathymetric full-waveforms of waters with different depths or turbidities is unavailable. Hence, an adaptive threshold selection (ATS) is proposed to improve the performance of RN reduction by adaptively selecting a threshold for each full-waveform based on the prominence of BR-to-noise ratio. The proposed method is applied to reduce the RN in raw green laser full-waveforms collected via Optech coastal zone mapping and imaging LIght Detection And Ranging (LiDAR). Compared with other traditional methods, the ATS improves the ratio of detectable BR by 5.64&#x0025; and achieves a root mean squared error (RMSE) closer to the real RN level. Therefore, ATS can effectively remove the RN, enhance the prominence, and ensure the fidelity of weak BR.

Systematic risk in the biopharmaceutical sector: a multiscale approach

It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil.