What is the effect of funding costs on the conditional probability of issuing a corporate bond? We study this question in a novel dataset covering 5610 issuances by US firms over the period from 1990 to 2014. Identification of this effect is complicated because of unobserved, common shocks such as the global financial crisis. To account for these shocks, we extend the common correlated effects estimator to settings where outcomes are discrete. Both the asymptotic properties and the small-sample behavior of this estimator are documented. We find that for non-financial firms yields are negatively related to bond issuance but that the effect is larger in the pre-crisis period.

Discrete GPU Market Outlook 2032

The global discrete gpu market size was USD 11.71 Billion in 2023 and is likely to reach USD 18.67 Billion by 2032, expanding at a CAGR of 6.5% during 2024–2032. The market growth is attributed to the demand for high-performance graphics processing in gaming, data centers, and artificial intelligence applications.



Surging demand for data centers and cloud computing services is driving the market during the assessment year. Data centers serve as the backbone of the digital economy, hosting vast amounts of data and running a multitude of applications and services. With the proliferation of data-intensive workloads such as big data analytics, AI, and high-performance computing (HPC), data centers require powerful computing infrastructure to handle the workload demands efficiently.

High demand for visualization and rendering in professional applications is a significant factor in fueling the market. Industries such as architecture, engineering, media and entertainment, and healthcare rely heavily on GPU-accelerated visualization and rendering for tasks such as 3D modeling, animation, simulation, and medical imaging. Professionals in these fields require GPUs capable of delivering real-time rendering and visualization performance to streamline workflows and enhance productivity.

Impact of Artificial Intelligence (AI) in the Discrete GPU Market

The use of artificial intelligence is reshaping the landscape of the semiconductor industry, impacting the discrete GPU market. The integration of AI technologies into various sectors such as gaming, autonomous vehicles, healthcare, and finance is fueling the demand for high-performance computing solutions. AI algorithms require massive computational power for tasks such as deep learning, neural network training, and inference. As a result, there's a surging need for discrete GPUs that efficiently handle the parallel processing required by AI workloads.

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DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

Snapshot img

Discrete Diode Market Size 2024-2028

The discrete diode market size is forecast to increase by USD 867 billion at a CAGR of 4.75% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The increasing demand for Internet of Things (IoT) devices and the acceptance of wearable technology are major drivers for market growth. Additionally, the miniaturization trend in electronic devices is leading to an increase in design complexity, necessitating the use of discrete diodes for power management and protection. These trends are expected to continue, fueling market expansion. Despite these opportunities, challenges remain, including price pressures and intense competition from alternative semiconductor technologies. To remain competitive, market participants must focus on innovation and cost-reduction strategies. Overall, the market is poised for strong growth In the coming years.

What will be the Size of the Discrete Diode Market During the Forecast Period?

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The discrete diodes market encompasses the sales of electronic elements that utilize semiconductor diodes, specifically those with distinct components rather than integrated into circuits. These diodes, which include rectifiers, switches, limiters, and various types such as power diodes and Schottky diodes, function by controlling the direction of current flow through pn junctions. Discrete diodes are essential in various applications, including consumer electronics, vehicle electrification for electric vehicles and traction inverters, and passive components in electronic assembly.



Their resistance to current and transmission properties makes them indispensable in numerous industries, contributing significantly to the growth of the discrete semiconductor sector. Electronic manufacturers continue to innovate and invest in research and development, with the data bridge connecting the market's dynamics and trends, including the increasing demand for discrete diodes in portable products and the integration of these components into national economies.

How is this Discrete Diode Industry segmented and which is the largest segment?

The discrete diode industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Power diodes
  Small signal diodes
  RF diodes


End-user

  Communications
  Computers
  Automotive
  Consumer electronics
  Others


Geography

  APAC

    China
    South Korea
    Singapore


  North America

    US


  Europe



  South America



  Middle East and Africa

By Product Insights

The power diodes segment is estimated to witness significant growth during the forecast period.

Discrete diodes, specifically power diodes, are essential components in various electrical systems due to their rectifying, switching, and limiting functions. Power diodes, which include Schottky diodes, fast-recovery diodes, and general-purpose diodes, dominate the market. Schottky diodes offer high-speed performance, making them suitable for high-frequency applications. Fast recovery diodes, with reverse recovery times below 5us, are ideal for high-speed switching applications. General-purpose diodes, handling low power and low frequencies, are widely used in consumer electronics. Power diodes' high adoption in power electronics applications, such as voltage clamping, rectification, voltage multiplication, and freewheeling, drives the growth of the power diode segment In the market.

Get a glance at the Discrete Diode Industry report of share of various segments Request Free Sample

The power diodes segment was valued at USD 1.17 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 72% to the growth of the global market during the forecast period.

Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The market in Asia Pacific (APAC) is experiencing significant growth, with China being a major contributor to the region's revenue. The increasing demand for semiconductors In the automotive, aerospace, electronics and electrical, and other end-user industries, particularly in developing countries like China and India, is driving market expansion. Industrial development in countries such as China, India, South Korea, Indonesia, and Taiwan will necessitate high levels of automation, further boosting the demand for discrete diodes. The APAC market is expected to register one of the fastest growth rates during the forecast period due to these factors.

Market Dynamics

Our discrete dio

This data release contains a comma-delimited ascii file of four same-day, discrete discharge measurements made at sites along selected reaches of Manowai'ōpae Stream, Hawai'i on December 8, 2021. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This data release contains a comma-delimited ascii file of four same-day, discrete discharge measurements made at sites along selected reaches of Ka'ula Gulch, Hawai'i on November 4, 2019. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This data release contains a comma-delimited ascii file of 16 discrete discharge measurements made at sites along selected reaches of He'eia Stream and 'Ioleka'a Stream, O'ahu, Hawai'i, on March 23, 2022. These discrete discharge measurements form what is commonly referred to as a "seepage run." The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This data release contains a comma-delimited ascii file of seven same-day, discrete discharge measurements made at sites along selected reaches of Olowalu Stream, Maui, Hawai'i on May 12, 2022. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This data release contains a comma-delimited ascii file of six same-day, discrete discharge measurements made at sites along selected reaches of South Halawa Stream, North Halawa Stream, and Halawa Stream, Oahu, Hawaii on February 4, 2022. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This data release contains a comma-delimited ascii file of three same-day, discrete discharge measurements made at sites along selected reaches of Hāhālawe Stream, Maui, Hawai'i on August 12, 2021. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This data release contains a comma-delimited ascii file of eight same-day, discrete discharge measurements made at sites along selected reaches of Kamaʻeʻe Stream, Hawai'i on June 13, 2019. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This data release contains a comma-delimited ascii file of six same-day, discrete discharge measurements made at sites along selected reaches of North Fork Kaukonahua Stream, O'ahu, Hawai'i on August 31, 2022. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

This data release contains a comma-delimited ascii file of three same-day, discrete discharge measurements made at sites along selected reaches of Honomū Stream, Hawai'i on August 11, 2020. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.